{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "84c41240-c6cd-4dce-882d-770a8f0eea33",
   "metadata": {
    "tags": []
   },
   "source": [
    "## SageMaker Model Monitor (data quality) for SageMaker Batch Transform Jobs - Model Training & Baselining Pipeline"
   ]
  },
  {
   "attachments": {
    "461c4520-413f-47c5-9bc1-2169108a70df.jpg": {
     "image/jpeg": 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aI8c+If2CvEnj6/trAaj8L9P1DTdLVIpBDLBoWlQyWxuFMhLsx4kKMgI6BTzX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\nDn9nGxb7Lrviu2t7e9jX5WW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ld34S1vTdHtfgtfa3MsUTW+qQLLIcKjuqomSegPCj618HUU6XFM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scbFt7D5sYIzzWdTMEp8qWmvzs0rL5u2p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\nKtWt7eWW4eO0sAsCurzFiXlTgE8DOcdjriceoSUUuur+V/vscOT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    }
   },
   "cell_type": "markdown",
   "id": "8ca4d9f5-5e32-4f66-8deb-de555e1add3f",
   "metadata": {
    "tags": []
   },
   "source": [
    "![AIM321-Batch-Lifecycle-Overview.jpg](attachment:461c4520-413f-47c5-9bc1-2169108a70df.jpg)"
   ]
  },
  {
   "attachments": {
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DmOM25Tl9zqy4wGBBBAr9p/25Pit8S/B37cvwG8MeFvFGpaVo17Jo7XNjbXUs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\n9lINyIbt1Un+JImuTgjtuZh6g181/s2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9nf8ATyX3/wDA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\nRXIZnCfDf4e6L8LvCdv4N8PzXFxZW81zOr3TI8pa6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NQX\nt9qmk6tafwpbFYRNBMuCplmiYZUhhgjgg1+svxw/4Qv4LeJ/Bfhm5+Nth8I/hX8NrW1kHhrQ7lx4\ng1eeIiQrdRwbZPJkXbn5JPMLOz8uGXx74A/HbSvip8Wf2mv20NJ099L0rwr4WisNLE4UTyRQwyT7\npNpI8x2tEYqCdu5Uy2MmeZtBynw18K0+E3iL9qn4w+OPE3wfv/iD4C0iTVXtdN8PWiC205Bclbaa\nWJHgjSIW8UgzuwCdwUkZX4ttPhH8SvGti/jXwN4I1S68O6lqbafZNa28t0n2mQ7o7RHVSZJApAwB\nk9a/Rz9k1m+Gn/BPf9oz4wSkx3PiVl8PxSH77b4ltwynr97UG5HdSe1erar8YvGP7Hv/AATc+FNn\n8PpY9O8W/EOW5uPtjoGkgtrppblp4Qfl80RPBGrEEAMSBnBFX7BY/Jv4m/s8fG74Nabp+s/FDwZq\nPhyw1RvLt57qLEbSbS3lllJCybQTsbDYBOODhfhh+zt8cPjPbzXvwv8ABWpeIbO3Zo5LmCEi2WRQ\nGKGd9se8Ag7d27kcciv1k8aXfjb4o/8ABO/4L+FviRrd1rnif4peNrSyt7q9kNxdJFcXl0kbl3JZ\ngqBduTwrhenFfUvxw/4Qv4LeJ/Bfhm5+Nth8I/hX8NrW1kHhrQ7lx4g1eeIiQrdRwbZPJkXbn5JP\nMLOz8uGVc4cp/Nh4q8J+J/A2v3nhXxlpVzoms6ewS4s7yJoJ4iwDDcjgEZUhgehBBHBBrnq+n/2w\n/jxpv7R/x98QfFDQrB9O0m5WC1so5gBO1vaxiNZJdpIDuQWwCdoIXJxk/MFWiGFFFFMAooooAKKK\nKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo\nAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/0fwIs/8Aj7g/31/nX3j+yf8AtTN+yzqPjHXL\nDwyNe1TxNpLaXbXH2z7IbEs28y48mXzPnCHblPu/e548O+CnxQ+GPgSxu7Dxx8PbbxbeXVwrw3U0\nihokwF8vY6OMAjcCME5IPQV9E/8ADQPwG/6I3Y/99w//ABmsHiaibiqTa9V/mfQYXJsJUpxqVMZG\nDfRxm2vujY8m/Zt+Outfs4fGHQ/izotp/aR0syx3FkZfIW7tp42jkiaTa+3IO5TsbDBTg4r2P4rf\nts+MfH37U2kftReG9Ij8PajoC2kdlp8twb2NYrdGWSJ5RHCWSbfJuwqkByAcjdVX/hoH4Df9Ebsf\n++4f/jNH/DQPwG/6I3Y/99w//GaPrdX/AJ8v71/mdH9gYD/oPh/4DU/+RPqzU/8Agpn8NrHW9W+K\nngD4E6do/wAVdatzFLrtxeC5EUhjEZkVBAjNlQAQGQt0ctzn5Q/Zj/bK8W/s/wDxh8Q/FnxDp3/C\ncS+L7ea31eK6uPJluGnlWYyibZJhwykYKFSpIwOCH/8ADQPwG/6I3Y/99w//ABmj/hoH4Df9Ebsf\n++4f/jNL61V/58v71/mH9gYH/oPh/wCA1P8A5E9I0D9u3wt8O/jr4c+LPwa+D+k+B9J0mwudMv8A\nSrS4LPqdtdOsjGW4EKAOjorRt5bEYwxZcAP+Jf7Xn7Nmp+HPFGnfCj9nXR9C1vxbbTW9xqmo3Buz\na/aVKyPaQBFEMgzuR43QBgCVI4rzT/hoH4Df9Ebsf++4f/jNH/C+v2fpv9f8HrVMdNjxfriNaPrd\nX/ny/vX+Yf2BgemPh/4DP/5E968M/t8/By9+Dvgf4TfGP4G2/jGH4fW0MWnv/ajRQSywxiMyywGF\nhmTBLgl1JJ+XHFfOf7Q/7ZnxP+P/AMQPD3jKVIfDOn+DJI5dA0ux5g094nR1kywHmSZjTLFQuFAV\nFGQdL/hen7O//RIYP++4/wD4mj/hen7O/wD0SGD/AL7j/wDiaaxdT/ny/vX+Yf6vYL/oPh/4DP8A\n+RPrK6/4Kb/DHWtb034r+KPgJpup/FXSrVLeDWGvf3KSR5KSpG0DOu1mJUbi6j5RJ3r50+DX7f8A\n8T/h18cPFnxh8a2cXjSHx5F9n1nTZn8iN4UJ8hIGKyCNYFJjRSrKUZlIJIYcx/wvT9nf/okMH/fc\nf/xNH/C9P2d/+iQwf99x/wDxNH1qp/z5f3r/ADD/AFfwX/QfD/wGf/yJ6t8Sv29PDSfCnWPg/wDs\ny/DK1+E2l+JiRq15b3Pn3lzE2Q0SssaFQykqWZnIQlVC5zXk3i39sLWdX/Z6+F/wI8IaJJ4Xk+Gu\noLqseqwXxkkuL1DM6TLEIY/JZXnd/wDWPyaf/wAL0/Z3/wCiQwf99x//ABNH/C9P2d/+iQwf99x/\n/E0fW6n/AD5f3r/MP9XsF/0Hw/8AAZ//ACIn7VX7WemftU6Z4T1TXvAsOh+OdBt1tb7W7a93pqUI\nXlWtfITy8S5eP96+wMy8ggjF/ZA/aX0H9ln4gX/xD1LwLD42v5bM2tl5l4LNrJnYGSWNzb3HzMg2\nZAU7SRnBIO3/AML0/Z3/AOiQwf8Afcf/AMTR/wAL0/Z3/wCiQwf99x//ABNH1upa3sX96/zD/V7B\nf9B8P/AZ/wDyJ9JeL/8AgoX+zx4/8Q3fizxt+y3pWuazfEGe7vNb86aTaAq5ZtPJwAAAOgAwOK+Z\n/hp+1D4M+HXwv+LHgBfhdpmo3vxGNytnfyyqTpMFwjIkKK8Ls4gLb4iHQ78E9BU3/C9P2d/+iQwf\n99x//E0f8L0/Z3/6JDB/33H/APE0fW6n/Pl/ev8AMP8AV7Bf9B8P/AZ//InxjX6v+If+CivwX8ce\nFPCPhT4ifs5WXiaDwTYpY6a13rzbYUWKKNtqCw6OIUyCT0AzXzd/wvT9nf8A6JDB/wB9x/8AxNH/\nAAvT9nf/AKJDB/33H/8AE0PF1X/y5f3r/MFw9gv+g+H/AIDP/wCRNr4x/t5fET4lz+CNM8K6Dpng\nTwl8PdQtdU0nRNMj/wBHW8snLwSSthd2zJAVVReSSCxyPqHXv+CpXgibxrpnxX8OfAzTIfHgW3hv\n9XurwS3DWsZAmgtnFuGQyRbo1lOSgOCjgYr5E/4Xp+zv/wBEhg/77j/+Jo/4Xp+zv/0SGD/vuP8A\n+Jo+tVP+fL+9f5h/q/gv+g+H/gM//kSpqv7YPiS8/a/P7XFjokcF+t7HcJpcs5mjW3S1FkYDMEQn\nMII3hBgnOK9P+M/7c3hDxz4q0X4hfC/4PaV4F8Z6Zr9v4hn1s3H228u7i33funKwQfupM5lUkhiA\ncAjNed/8L0/Z3/6JDB/33H/8TR/wvT9nf/okMH/fcf8A8TR9bqf8+X96/wAw/wBXsF/0Hw/8Bn/8\nifTnjH/got8DvFmqv8R779m/RNR+Is0Co+o6jcpc23nLGI1keH7ODLtCgKGYOFAAkGMn5T+KP7Vp\n+If7NPgL9m7TfCiaBp/gy8kv5bqK8MyXk8nnnKwGJTEA1xIcGSTr171of8L0/Z3/AOiQwf8Afcf/\nAMTR/wAL0/Z3/wCiQwf99x//ABNCxdT/AJ8v71/mH+r+C/6D4f8AgM//AJEyPEP7U7av+yJ4a/ZT\n03wyNOTRdVfU7vVxeeYb4tJcSCM23kr5eDMnzea3+rHHPHzz8PPFFr4I8f8AhrxpfaeNXttA1Oz1\nCSyMnki5S1mSVoTJtfYJAu3dtbGc4PSvpz/hc/7M3/RJ/wDyIn+NH/C5/wBmb/ok/wD5ET/Gj67U\n/wCfMvw/zD/VzCf9B9P7p/8AyJ1Wu/twv4j/AGytL/az1bwWJrfR40jttBOo8KI7N7df9L+z9pnM\n4/c9fl/2q8mtv2kXf9rA/tQ654eGoka++uLpJu9gXDlreH7T5Tf6nCAP5XO3O0ZwOv8A+Fz/ALM3\n/RJ//Iif40f8Ln/Zm/6JP/5ET/Gj65U/58v8P8w/1cwn/QfT+6f/AMidT4U/bgbQv2w9c/a21fwW\nNWuNUSZINKOo+V9lElulrHi5+zvv2Qpt/wBUuc9q+f8AwX8fvFngb9oSH9orSoo5dcTWbnV3hlJa\nOU3ryG4iZhg4kSV0LDkZyOa9X/4XP+zN/wBEn/8AIif40f8AC5/2Zv8Aok//AJET/Gj65U/58v8A\nD/MP9XMJ/wBB9P7p/wDyJ9D+Nf27/wBmvWtc1P4jaJ+zXpNx481lXae81i7+2WYuJPvStaiJUkY5\nJLjynJ53Zr5k139qltS/ZF0P9lTS/C66amnas+qXmrLeGT7YGlnlWL7MYRs2mWMbjM/EQ4+bjU/4\nXP8Aszf9En/8iJ/jR/wuf9mb/ok//kRP8aPrlT/nzL8P8w/1dwn/AEH0/un/APIk/wCyP+1zb/s4\n2vi/wd4v8Jw+N/BHjmBIdS02SURNmNXTcrMjqwZJGV0IGflIZSOdD4tftN/AfxJ4bsvA/wAJfgPp\nPg/SItTttRu7ua5a71O5W3fPkpcMgaJHUsrBjKuGOFB5rI/4XP8Aszf9En/8iJ/jR/wuf9mb/ok/\n/kRP8aPrlTf2Mvw/zD/VzCf9B9P7p/8AyJzP7XP7Tl3+1b8V7f4lXuhf8I9Baadb6dDYi7+17I4X\nklY+d5UPLvKx+5x716x8XP2+fE/j79oH4f8Ax48G+GYvCjfDuzis7LTJLs30Mih5fO3SCKAhZopP\nKIC5AGQ2enG/8Ln/AGZv+iT/APkRP8aP+Fz/ALM3/RJ//Iif40fXKn/Pl/h/mH+rmE/6D6f3T/8A\nkT6J+IH/AAUC+BHimbV/GWl/s36GfHmuwSxXGpancLdwrJMmx5TEtvGZHIyNwaN/9rk55zwX+3T8\nKtU+CHhX4O/tG/CCL4if8ILF5GkXi3xtGEKALGj7U3JtRVRirEOFXcmRk+M/8Ln/AGZv+iT/APkR\nP8aP+Fz/ALM3/RJ//Iif40vrlT/nzL8P8x/6u4T/AKD6f3T/APkTyT9oH4veH/jP42g8SeFfAmk/\nDzSbGzjsbbTdJTCeVEzsJJ3Coskx34ZxGmQFyCRk9n+yh+1N4l/ZX8c33iTTNKg8Q6Nrlr9i1XS7\nlvLS5g3bhtk2vsdTkAlWUhmBU546j/hc/wCzN/0Sf/yIn+NH/C5/2Zv+iT/+RE/xp/Xam3sZfh/m\nL/VzCf8AQfT+6f8A8ifTXhj/AIKKfBX4NandeIf2ef2frDwvrOrbI765n1KSRWt/MEkkEcaRgIGI\n+UqQqkA7CBivkX9qr9oL4e/tDeJLPxb4R+GsPgLV5Jry51a5jv2vX1Ke6ZGDSboogvl7Wxgc7zwA\nAK6D/hc/7M3/AESf/wAiJ/jR/wALn/Zm/wCiT/8AkRP8aSxlT/nzL8P8wfDuE/6D6f3T/wDkTI/Z\nP/amb9lnUfGOuWHhka9qnibSW0u2uPtn2Q2JZt5lx5MvmfOEO3Kfd+9zx8k19m/8Lg/Zcl+af4Vy\nIw4wki4x+Ei/ypf+Ft/srf8ARLp/+/g/+O1X12f/AD5l+H+Yv9WsL/0H0/8Ayf8A+QMdP2pmtP2Q\npP2U9M8Mi2F5q39qXusC8z9oAkEixG18kbcFIhu80/c+7zx718Mv29vA8HwX8N/Bb9ob4T2nxM0/\nwW6nSLh7oW7IkYKxxyoYnDbEbZkEBkChkYjcfHf+Ft/srf8ARLp/+/g/+O0f8Lb/AGVv+iXT/wDf\nwf8Ax2l9dn/z5l+H+Y/9W8N/0H0//J//AJA1vit+3d8R/iR8bPBfxVsdOttB0f4dXFvLoXh+Bi1l\nbxwMpZXIVNzSqojZlVcIAFC45+qdQ/4KmeBdO+IqfFTwP8CtNsfFGp+UmtancXqtfXVvGgQwQzLb\nfu8hEBkIbKrgpnDD5A/4W3+yt/0S6f8A7+D/AOO0f8Lb/ZW/6JdP/wB/B/8AHaPrk/8AnzL8P8x/\n6t4b/oPp/wDk/wD8gd78Kf2+NU8B/Hj4m/FLxF4Ti8QeGviu8y6roctwPlgZn8iNZ2iIfyonaIho\nwsik8Lxjm/jR+018AvE/w91b4c/BT4EaT4JTWpopbjV7mdr7UUEUqyhbZyqtACV2sBIyFSRsHWsb\n/hbf7K3/AES6f/v4P/jtH/C2/wBlb/ol0/8A38H/AMdo+uT/AOfMvw/zF/q3hv8AoPp/+T//ACB0\nnxI/bR0P4qfs0eF/gR4u+Gtnda74O0+DTdL8RfbWElrDAIU3xWwhGHkjgRJMyEEc45wPmH4H/Ei1\n+D/xb8K/FC80ca/H4Yvo74WJn+zec8OTH+92S7Nr4bOxunSvev8Ahbf7K3/RLp/+/g/+O0f8Lb/Z\nW/6JdP8A9/B/8dp/XZ/8+Zfh/mH+rWG/6Dqf/k//AMgeJfH/AOL178efjF4o+Ll/Y/2ZL4juVmFr\n53n+RHHGkMcfm7I9+1EUZ2LnHQV6r+yp+1M37LsvjfUrDwyNd1XxXpLaXbXP2z7KbAsWYy7fJl83\n59jbcp9z73PGx/wtv9lb/ol0/wD38H/x2j/hbf7K3/RLp/8Av4P/AI7R9dn/AM+Zfh/mH+rWG/6D\nqf8A5P8A/IF/9lH9sb/hnvw54r+GnjPwjB478A+Mxm+0yWb7O6ylPKd0fY4IdMBlIzlVKspB3b3x\n9/bdtviD8Lrb4EfBPwNbfCz4fCUT3tnZz+bNfuCCBNIscfybgGYEMzsqlmIG2uS/4W3+yt/0S6f/\nAL+D/wCO0f8AC2/2Vv8Aol0//fwf/HaX12f/AD5l+H+Yf6t4b/oPp/8Ak/8A8gZH7Xf7VEn7U3in\nw3rVt4ZHhHTPDGlJpltYLefbRlZHdpRJ5MGNylF27TjZncc4HpPj/wDbkt/HXj74H+KT4DWy0b4J\nx2y22l/2l5gu5LYwlX877Mvk/wDHvFxsfG3qa43/AIW3+yt/0S6f/v4P/jtH/C2/2Vv+iXT/APfw\nf/HaPrs/+fMvw/zD/VvDf9B9P/yf/wCQIda/bF8Sah+2AP2uLDRI7e+ju4po9LluDNGtvHaCyMHn\nCNCd0QPziMYY5x6+ifG79uHwh8QPEmj+Pfhd8INL8BeNNN1638Qza79o+23tzc2+SI3Igg/dOTmR\nSWDkA4B5rgf+Ft/srf8ARLp/+/g/+O0f8Lb/AGVv+iXT/wDfwf8Ax2j67P8A58y/D/MP9W8N/wBB\n9P8A8n/+QPrO+/4KcfDGXxI/xis/gJpo+Lb2qW41ua982NJEi8kShfID8J8mAyvs+QyYFfMPwh/b\nf8V/DmT4x+IfE2kP4s8X/FuxNo2ry3v2ZtPcxzp5ixCGQSKDKhWMNGFWJVBAxjK/4Wx+ylJ8kvwv\nuVU9Ssoz+kw/nR/ws79kb/oml9/39P8A8k0vrsv+fMvw/wAw/wBWsP0x1L/yf/5A+Ma+uf2S/wBq\nhv2VtU8X6/YeGBr+qeJNKbTLaf7Z9kNiWbeZMeTL5nzBDtyn3fvc8av/AAs79kb/AKJpff8Af0//\nACTR/wALO/ZG/wCiaX3/AH9P/wAk03jp/wDPmX4f5iXDGH/6DqX/AJP/APIHxjX1voH7U7eG/wBk\nTxD+ytpnhkRt4m1VdTu9ZF5gsEkt3EX2byfS2Rd3ndM/LWv/AMLO/ZG/6Jpff9/T/wDJNA+J/wCy\nOCCPhpfAj/pqf/kmm8fP/nzL8P8AMP8AVjD/APQdS/8AJ/8A5A/Tv9pf4p/Ar9m74Z/BH4F/GX4X\nL8RZ9E8OWt9HC961oltdRxpAzSRhGEgmkWXduyBgjac8fl/+0F+2v8TPjt8RvDHjWK2t/DGmeBJ4\nbjw/pFp89vZSQOkiuxIXzJMxoCdqqFUBVUZz2vjb9pH4BfEvVYtd+IfhXXvE2pQQrbR3OpX8t3Mk\nCMzrGHlu2YIGdiBnGST3Ncf/AMLO/ZG/6Jpff9/T/wDJNTHGz/58y/D/ADKfDOH/AOg6l/5P/wDI\nH0z4z/4KCfs7fELV7b4l+OP2cdP174hxQRxyXV1f7rJ5IkCo8kJhYSquBtWRWIUBQ/Ga8W+A/wC3\nHY/Ca0+KGgeKfhnpnifw18Trt72fSIJ20+0tXkL7oYwY5ybfDhVTIKhRhq4//hZ37I3/AETS+/7+\nn/5Jo/4Wd+yN/wBE0vv+/p/+SaPrsv8AnzL8P8w/1aof9B1L/wAn/wDkBPF37W1prf7Lqfsy+GfB\nSeHbJteuNZmvY79plkiluJp47UW7QqQIg8SBzKxIiBK5b5cr9pv9qh/2iPDnw18J2fhkeF9K+G2k\n/wBmW8IvPtgmJjhiMv8AqYdnyQIAvzY/vVrf8LO/ZG/6Jpff9/T/APJNH/Czv2Rv+iaX3/f0/wDy\nTT+vS/58y/D/ADF/qzQ/6DqX/k//AMgS/FP9snV/HPgb4JeDfCfh0eFm+C8cDwXH2v7WL69tktxH\nctGYYhFh4WfZufPmEbuMn6U8Z/8ABQT9nb4havbfEvxx+zjp+vfEOKCOOS6ur/dZPJEgVHkhMLCV\nVwNqyKxCgKH4zXzN/wALO/ZG/wCiaX3/AH9P/wAk0f8ACzv2Rv8Aoml9/wB/T/8AJNL67L/nzL8P\n8w/1Zof9B1L/AMn/APkD5T8X+IB4t8V6z4pGnWukf2xeT3n2OxQx2tv58hk8qBGZisaZwi5OAAK5\n2vs7/hZ37I3/AETS+/7+n/5Jo/4Wd+yN/wBE0vv+/p/+Sar6/P8A59S/D/MX+rGH/wCg6l/5P/8A\nIHxjRX2d/wALO/ZG/wCiaX3/AH9P/wAk0f8ACzv2Rv8Aoml9/wB/T/8AJNH1+f8Az6l+H+Yf6r4f\n/oOpf+T/APyB8Y0V9nf8LO/ZG/6Jpff9/T/8k0f8LF/Y7k+eX4camrHqFlbH6Xg/lR9fn/z6l+H+\nYf6r0OmOpffP/wCQPjGivs7/AIWF+xv/ANE61X/v83/ybR/wsL9jf/onWq/9/m/+Taf1+X/PqX4f\n5h/qrR/6DqX3z/8AkD4xor7O/wCFhfsb/wDROtV/7/N/8m0f8LC/Y3/6J1qv/f5v/k2j6/L/AJ9S\n/D/MP9VaP/QdS++f/wAgfGNFfZ3/AAsL9jf/AKJ1qv8A3+b/AOTa87+Jviz9nbWfDbWnw18HX+j6\nyZEK3E8zeWqA5cFTPMGJHA4GM5z2qqeNlKSXs5L7v8znxXDlKlTlUWMpSstk5Xfkvd3PnWiiiu8+\nYCiiigAooooAKKKKACiiigAooooAKKlgt57qVbe2jaaV+FRAWY/QDmu0vNL8N+GFns9VlGs6uvkl\nUs5lNlCchpElmUEzNgbCISEBO4StjbWtOk5JvZDSOGortLjxmZDdraaHpVnFdMjBEtfM8oJjCxvO\n0kgBxzliTzk8mluPHN9cC7DaXpSfbEVG2adbJsCgj93hPkJzyy4J454FX7On/N+A7LucVRXaz+Ot\nQuGvGbTNKT7ZCsB2abbII1Xf80eE+RzvOXHzHC8/KMOl8d30rzO2laSvnQiAhdOtlCrz8y4Th+fv\nj5uBzxT5KX8z+7/ghZdziKK7dPHd+jo40vScpD5AzptsRjj5iCmC/H3jz+ZpIPHV/byW0i6XpLfZ\nYDAqvpts4YHZ8z5Q73+QYdskZbB+ZsnJS/mf3f8ABCy7nE0V2tv45vrYWgXS9Kf7HGY136dbPvyF\nG6TKfO3y8Fs4yfWkt/HF9braoumaU4tNxXfp1u5feCP3hKZfGeN2ccY6CkoUv5vw/wCCFl3OLort\nI/HF/H9mA0zSiLWWSYZ062O9pA4IfKfMo3nah+VSFwPlXCr45vlEY/svSj5c73HOnW3LOWO1vk5j\nG75U+6MAY4oUKX8z+7/ghZdziqK7QeOL4KV/szSsGf7R/wAg63zuznZnZ/q/9j7uOMYofxxfyLIv\n9maUvmzx3Bxp1sDmMqQg+T5YzsG5BgMC2c7myclL+Z/d/wAELLucXRXay+Ob6UXAbS9KH2mSORtu\nnWy7fL2jamE+RTs+YLjOWz1NJN44vpvtedM0pftm0Nt063XZtGP3eE/d577cZ69aHCl/N+H/AAQs\nu5xdFdpdeOL+6W8VtM0qP7bEIWMenWyFFAYZiwn7tvmPzrhunPyjD5/HmoXD3LnS9JQ3MIgOzTbZ\nQgG75kATCv8AN94c8D0FPkpfzP7v+CFl3OIortz47vjMZhpWkgmHyMf2dbbQuc7gCmN/+3973oi8\neX8Lwuml6STBCYF3abbMCDj5mDIQz/KPmbJ6+pyezpfzP7v+CFl3OIortbbxvdW4s1fSNKnWyhMK\niSwh/eAhRukZQrO/y/eYk8sepzVi2vfAusx2tjqli+gSxxuJL20L3KyS4QIZIJH+VeHLeW3VhtXC\n7SKjB/DL79P+B99h8q7nBUVta14e1bw+1qNUhCJewrcQSI6yxSxv3SRCytg5VsH5WBU4IIrFrCcH\nF2krMloKKKKkR//S/Aiz/wCPuD/fX+dfenws/Zu8C+KPgpcfHT4rfEkfD/Qf7ePh+0H9jz6rJc3K\n2wumYLBKjKoUkZ2kZGM5wK+C7P8A4+4P99f51+7Xw78Razof7I/wn8F/B74mfDXSNSaTVtT8RWni\nrUdKaZLm5uP9DAt7xJfLdINyscK3QdK1pvQiS1Pzgf8AZ+8UeM/FOsad+zvaat8VvD2kCHOrWGjX\nVurNJGrsrQtvaNlYsoDNltu4DBrlfCvwK+NPjm81XT/B/gTW9YudClkgv47bT7iRrSeI4eGYBPkl\nUjBjbDZ4xX7HfBfxD8PbTSfgJqTfF7wx4R8GeFzqOs+JdMj1KGzvNS8S+ZJKRLbRlFaJdq+S0hCb\nAqovzRg+KfEXxBdfGP4D+BtX+F/xg8O+A7C1i1bWPEum3etf2Zqdz4hvbt5pC8EQ824Z8hYGb5Qv\nJKryNOYnlPk/4rfsf+OvBXw98DeOfCOh634ht9b8LW/ibW7mGxeSy02O7LtGhljQgbYl3ybjlR8x\nwvNeV/s5fBOT4/8AxPtvh+2sr4dsvsd9f3mpPAblbW2sbd5mcxB4y2Sqp94Y3Z5xg/qF4m/aS8Op\n8fPjK2neP7WXwB4B+Hd3ofhKxXUYvsN/evZ29ui28av5dwzSmUkoHbaq/wAKjHxn+xd8RPCnwb0L\n4y/FXWZtJudZsvC40rStI1WRcalLqtzGkiLbiSOWdESP96IzlUY5IBzQm7A0rl1P2LfDupS+AvFX\ngz4it4u+H3jTUrrTJNT03Qb99Ss5rK3e5mB0oB5pAEjb5kLAAFj8vX5tT4A/F7VbO+8Q+EfBeva1\n4atoZL2HVI9KuVgm08O6pc52soVgjHhjja3Pyk190fBL9rnVPFN/4t8S+Nrnw94K0r4e+Cde/wCE\nU0DSYYdKszrOpKkCfZoC5eWZ1d92GdgMkDlifsTxx4qX9mbxH4V1Xxh8Q9PsfDPwo8AQaNaeDrbU\nFl1DVfEF1Y+W7XOnIcqh81HaSbgBVZQAxai7CyPw20b4OfFjxD4Mv/iNoXg7Vr/wtpiu9zqkFlNJ\nZxLF/rGaYLsxGOXOfkHLYFfT3jH9lDwh4PuLey1fxJqtrJa/Dm18Y6k8Oky6ilvqt7/qdOkNvgW8\nTZGbiYhFPBPIr9A/i98X/BelfBjTB8GfEfhTTPDl14T07w21xc+Inm1OLT7vYl/b2mgwoUiuxkme\n4lJcgHkHAqn8WPF/wnvvFfxiZ/H/AIYubP4qa74F8OaebPWLWdLPw9aPGbyWTa/7uIJGfOLfKmRk\ngkUczDlR+PuqfAL446HoGo+Ktb+H+vafo+kFBeXdxptzFDb7wrL5jugCghlOTx8y5+8M5g+DnxYb\nwE/xSHg7Vv8AhD4yM6v9imFlhm2BvO27Cu/5d2du75c54r9TvF37T+jeMfE37VvirxT44jv9Ba0t\n/C3hzShfoyXlhdX32eafTrbeFl2wxGZpIwcCTcWwc12P7cHxn0m3+G/im3+DOu+Eh4Y8T2ulaTGb\nbxG+pancaTbqgS3sNIRBDp8MbBvPLHe3POdoU5mHKj8dvB3wp+J/xDha48A+EdW8SRJOtsz6dYz3\narO6s6xsYkYBiis2D2BPQUN8KPiguuxeF28I6sNYnsv7Sjs/sM/2h7HYZPtKxbNxh2AtvA24BOa+\nvNJ+L8nwm/Yf0Lwf8OvGf9keKvHPi691DV10u8CajbabZwR28cU6wuJoklcCRc7d4BwSuRX3J8Vt\nc+FfhP4h/Gn4+aV8T/C+pJf+AV8OeC9JsNThuL4JPaW1qZmUECN45N+2PlyrOxAVCS+YVj8fNN+A\nXxx1jRZfEml/D/XrrSYbJNRa7j025aD7FICyTiQJtMbKrMGBIIBPQE1J8Efhvo3xQ8ZzaH4k1S90\nTRrDT73Ub2+sNLuNXkt4bSIvua3tvmEZbarSEhEByxAr9Y5/2ifDXhz9pDU9F0r4iWUvw8+DXw1m\ns9Ehh1OL+zNV1GLTIohFbhX8u5nd5SqqNzZiwPu18L/spa/4X8H/AAv+PGvajrthpWv6v4ctfDGm\nW91cxwz3C67drDdvCjsGdYI4w8rKCEXlsChNhY8Ah+AXxxuPD134sh+H+vNotjaR3897/Ztz9nS0\nljEyTmTZt8sxkSbgcbPmzt5rO8P/AAZ+Lfizwnf+O/DPg3V9U8OaWkktzqNvZTSWkSRAmRjMqlMR\ngEvg/KBlsCv1t8T/ALSXhxPj38ZDp3j61l8AeAPh1d6H4Tsl1GL7FqF89pb26Lbxq/l3DNKZSSgd\ntqr2UYwviHJ4a8T+IfhD4j8NfGbw3onwV8P2vhfTz4eTWfs9+Y5Z4k1MXGnQjHmYeR7iSYgbAck9\nKXMx8qPgn9pL9nG++C/xD8ReD/Cseo+INO8FWWkPr2ofZy0FjeapAkixyyRLsjRncJHvILMCOTXl\nGjfBX4v+Ide07wvongrWL3V9XsI9Us7WOxnaWfT5v9Xdxrty0Dfwyj5D61+sPjweDvihoH7Rnhf/\nAIWp4Q0fUPGHjzT765v59Xhe2k8NWcfnWr27RljcyRMVQxRgkSKUyMgn074meNPht468OfEHwT8H\n/i94c8F3Fjqvh3w/ca3PqcUDyeD9L0uMsLCSJi8+b55QY4WAflWIDcnMw5T8x/i1+yL4z8E+I/AX\nw68F6RrHiTxx4h8MWmv6vpUNm0s2nS3Ujr5DJGpZPKCASGTG0kZIryDR/wBnT4+eILnWrLQ/h14g\nv7jw7KYNRjg0y5ke1mADGKRVTIk2kNs+9gg4xzX7nt8a/gz4v1X4vW3hPXPDuo6qniHSLKD+0/FK\n6DZ3WgaTp0CQy/boRI91bJe+e8kURAkD/MxU7X8Ll+JU3jz4YfEq5/aC+InhL+z7271PxDour+E/\nETW2rW2tQR/ZLa1j09ds1xDJHCvlCVdwjIZmLEFFzMHFH4teG/DHiPxlrlp4Z8JaXc6zq9+2y3tL\nOF555WAJISOMFjgAk4HABPSvuxP2JLbw54W8AXnxa1HXPCGt+J7XxJqWq2iaLPqUul6foe1Y5ZLW\nACZVmZ1JkciNVIYkDmnfsP3+j2Pgv40RaL4w0bwP8SdR0jT7Lw/qetXsenRQWs9y39qNDcvlklEC\nrtKDeDgjpkfWHxS+Lvwy8MeCfHtl4c+IWmeKLrwp8KNG8Gafcx30ck+rXuvXTHVJ7dGkaR2SNY2m\nUZaLo+DTbdxJI+d9B/4J9a23wu8HXvibR/GC/EH4hXHlWVrZ6OTpWiR/aVhEmtzSYlhDRFphtGVV\nTuGOa8s+PP7J8/hX4rJ8G/gZ4a8beJ9e0yKdtRbUdIESXKRzGFL2wWDcxspHVwssmASBtY5r7WtP\njv4V0v8AbT8AxzfEi3n8C/CbwRbW0tymsqbDU7yz0qV9qyiXyp53nnVCMszMhTkjAj/ZM+MXh7WP\n2etXn1jxBot58Qp/Fv8AaOojxR4ok0GP7JbxCSzkkdQ811ZQzuzfZUIUyE5yDhldjsj8o7H4FfGz\nUzfDTvAOvXX9mT3NrdmPTLlxb3FmqvcRSkR/I8SspdWwVBGcZFEnwL+NEPhrS/GMvgXW00PXJobe\nxvDp84hupbkhYFifZhzKSBHj754XJr9KviH+0xGug/Bjw7rXj+0u4fF/i7U9f+Ih8P3e23ktZdTj\nhjglWJhKsBslbEUuCyBSwJANfWvjL4xeGJ/jh4btJ/GvgrSvAGveLLfVtQu/+ErGq313baHDLfab\nIY2VLXTrMSwxKsKHe0zIAXy7M+Zhyn4X3f7O3x9sbOzv7z4ceIoYNQuzYW7NpV0DLdglfJQeXkvu\nVgFAySpA5Bxq/H79nL4l/s4eJ7bw18Q7B4xewRTW95HDMtpOzwxyyxRSyxosjwGVUl2ZCtxmvv3w\nH+0rZ+MPh3rfir4pePwuo+Pfiv4eSS1uL8fatL0KwlW8kuo4A2+C3QHyt6KFyu3ORXyr+3ZqOq63\n+0J4i1yfxtpfjHSdZvLq+0v+yNUGp21lZyTGOCJypMcMpijjZ0UkcjJPZpu4mkQfEH9kXxp4b+C/\nw9+KfhPRta8Qf8JFotxrmtyQWTy2OmWxmxanzY1ON0IMj7zwPm4XJrxPRfgL8bPEfg6T4heH/Amt\n6j4aiV3bUbfT55LbZFne4kVCCiYO5hkLg5Iwa/Z/x38WPA/ws1ubxZefFzSNY+HfhzwHB4T8MeG9\nE1OPUJr67uLSO2muZ7OFtiRxnduaX5iEAA4CnP8AjR8QvB/g60+NvjnRviz4c1DQbvwVD4W8C+Fd\nG1WOY22n3wt7eWVbVCFSVTubChpAm4kqiCp5mNxR+VXxM/ZO+Nvwn8F+EPG/i3w5ew23i5Dth+x3\nKzWM7TNFDbXYeJVjnnC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B1/4E/wDIP9Ypfyfifzaf8ID4\n7/6FvU//AACn/wDiKP8AhAfHf/Qt6n/4BT//ABFf0l0Uf8Rlrf8AQOv/AAJ/5B/rFL+T8T+bT/hA\nfHf/AELep/8AgFP/APEUf8ID47/6FvU//AKf/wCIr+kuij/iMtb/AKB1/wCBP/IP9Ypfyfifzaf8\nID47/wChb1P/AMAp/wD4ij/hAfHf/Qt6n/4BT/8AxFf0l0Uf8Rlrf9A6/wDAn/kH+sUv5PxP5tP+\nEB8d/wDQt6n/AOAU/wD8RR/wgPjv/oW9T/8AAKf/AOIr+kuij/iMtb/oHX/gT/yD/WKX8n4n82n/\nAAgPjv8A6FvU/wDwCn/+Io/4QHx3/wBC3qf/AIBT/wDxFf0l0Uf8Rlrf9A6/8Cf+Qf6xS/k/E/m0\n/wCEB8d/9C3qf/gFP/8AEUf8ID47/wChb1P/AMAp/wD4iv6S6KP+Iy1v+gdf+BP/ACD/AFil/J+J\n/NDqfhrxJokK3Gs6TeWETttV7i3kiUt6AuoGaxa/pX8VeFfD/jbw/e+FvFNlHqGmahGY5oZBwR1B\nBHKsp5VhgggEHNfhv+0V+zr4g+BXiDehk1DwtqEhFjfEcqevkT44WVR0PAcDIwQyr9rwh4h0cyqO\nhWjyVOivo/Tz8j0cvzaNZ8slZnzhRRRX6OeuFFFFABRRRQAUUUUAFd14x+HfibwPa6JqOs2/+g+I\nbG3v7O4TJjkjnjWQpns6bsMp579CCeFr9uPDXgPw18SP2f8Awj4W8U2wuLS40LTSrDiSGQWqbZI2\n/hdex78gggkH6fhvh/8AtBVYRdpRSa7ejOrDYf2l11PxHor174x/BzxL8HPEraRrCm4sLgs1lequ\nI7iMf+guuRvTPHUZBBPkNfP4rC1KNR0qqtJbo55RcXZhRRRWBJ2viLOqaFouvxyT3DQwLYXbSKNk\nUsBYQIjKqjabcJgHJyrcntzGl6nf6LqdprOlzG3vbCaO4glXGUliYOjDORkMAa3PDWvWunGbStcS\na60O+INzbwybGMiK6xSqTxviLllB4PRsgkVDq3he+0u3iv4ZoNSsZIopTcWjmSOPzcgJKCA8T7gy\n7ZFXJUldy4J6qsede0Xz/wA/n+fyLavqfe5/be+Ftz42g+OmqfBGyuvi/biOX+1hq06aY+oRRhFv\n200R7fNBAbaJMZ+bdvG6ub8Cftm+F4PA8Hhz42/DgfETVdK8TXfirTr8arLpey/vnEsxmSCJvMUy\ngvjIByFxgZr4Iork5UTzM+9tZ/bN8LeM7f4raL8Qfht/bOh/EfXE8Q21tFrElrLpl/HEYkJnS3Pn\noFI+UomcH14+nvjP+2B8H/hP8QNF8WfDfwTY+JviTYeDdN0+z8WQ6489raSz2PlOGsYlaEzQKxQj\neDyQ2MYr8a6KXKh8zP0t+G37evgDwZ/wrLxT4l+EA8R+PPhfpK6Fp+rrrk1lGbGNJI0zbJBIhk2S\nupL7xyWHJG3541n9oXwv4q/Zy0D4H+LfA/27WPBkuovoOuRalJB9kTVbpbq5WW0ERWcll2gtIMDB\nAyOflminyoVz7E+E/wC0n8NfCXwIuPgN8U/hg/j3SZPEreJopE1yXSdlx9jSzVGWG3kdlCq5J8xc\n7ugKgn1rw9/wUK1Cz8favrGt+CIYvCF54aj8KWGjaLqEulzaVp0EgkjFtfIjyhyc+YwCl/l6BFFf\nnDRRyoLn6P6t+31pGsfFWHxpc/Dhm8MzeA5fh/f6PNrk9zPc6bPM8ryDUJIPOWQhlUlldjgkvlsj\nqPhl/wAFEfB3w08P+FPD9h8K7s2vgO+1CfRbe28UXVnbi31Cdpil7HHb7LySINsSSVSDksVyTu/L\niilyoOZmtr2qHXNc1HWjH5J1C5muNmd2zzXL7c4GcZxnFZNFFUIKKKKACiiigAooooAKKK2tB8Pa\nt4lv49N0iESSuSCzusUSAKzlpJZCqIoVWYliAACe1VCDk1GKuxpX2NLwRYQX3iaya9uFs7O0f7Tc\nTunmJHFB85JXIB3EBFBI3MwXPNYOqajdaxqd3q982+5vppJ5WAxl5WLMce5NdRqWq6fo+j/8I74Y\nup3+3xINWlcIsc8kb70iiABbyY2AOS37xwGKjamOJreq1GKpr1fr/wAD82xy0VgooormJP/R/Aiz\n/wCPuD/fX+dfeXwm8XW/wq+F1l4qvELweIfEyW15GBkzaZaWrLOAOM83BABPUfjXwbZ/8fcH++v8\n67uvWynHyw03Vgve6eX9LQSqOLuj710vxH4Q1j4cw+HLJX0/wFqPifSNBgjnbbLJbwN9rvLmdlxi\nSZmTcRwi7QOFrrZNM1PWLnUfBPxrjg0zSNX8RafbeHdOxHD5VvBOxmMAjG5Yng2xbzwzNnOev5u0\n5ndzl2LHAHJzwOlfQQ4n0XPTvZWteya10atsrvlXTzNFiu6P0oDeJdQ0RPDvxdKaXpXifxXYabb6\nSDHHHZ6fa77gReXH/q1kYRLz8xBVuhBrRj1LXZPiJ4T8L6noV7DaWeoahq1o2ox29tIz2NpIsNpY\n2kLN5VuGKkEsxkPOflNfmMzM7F3JZmOSTySaV5JJW3yMXbgZJyeOlbriy1vcejW8rtpO9np3vba1\nyvrfke7fAjT5br44aXd6+rKdHnudSvC4+ZGsY3nYt7+YgH1r0fwB458JfEHxpZ/Dqx8NpoOj+LtT\nS61l5r2S7uL9rYvcRQGRljVEaXjCqCSRzXx/QCQcivHwecSoxjBRuuZt7ap2Vr2utE07b3ZhCtyq\nx+kdrHr2v6T4a8PfG7ybNPEXin93pjGNIrOysrZpEtQiY8oSStGm04YqVJ+9msfwZqHj+x8Qa74j\n/aAin0jw3pml3Jgs4lijFouoTR2f7iKP5kUI7BSeWxkbjnP56szMxZjknkk9SaV3eRtzsWY9ycmv\nT/1n1UuV3XeW+mnNprZ6pabmv1rrb+vM/QiTSPHcWp+Mr/QdPtLjV/DcFhD4UgtFT7HaaZfu5N/b\nrIdjExoN0zElXLFjgDGr4lg1m61rXbnSprTVviB4U0LQrCK5uJYMJJdK017fK0xWNmUOoDnOwHPp\nX5xKzLnaSNwwcdxXongn4gWvgyFg/hbSNbuVl86G41CKWR4nwAAAkqIyjGQrKeTn2rWjxHTk+WUX\nFa3d7/zWa93dXVn/AHVt0pYlPRn31d6f9q8Wa+buW61jxp4R0TQ9NE+mwRXd8ZbgPLd3MCzlF3tu\nVPN5ManOK+TPEPii31z9o7TtZ8caeukWtrqmnw3kUsiTssVs0aM1xIvyyMVXMpHuO1eG654i1nxH\nrt74k1e6efUdQleWaXOCzP1xjoMcADgDgcViVyZlxD7ayhGyUr6vfV2v52dnrrb0tFXEX2R9y6/8\nMrg+IfiZ4r+K/huS7s/L1DU9P1k3jpBIeRZwxCNtrh8rtBOVUYxxWzp2qrZ/ELwL8E7uO1GlWuh2\nZvrV4owb7Ufs7X0ME0xG/wAvz2TCbgpJIIO6vgZp5njWF5GaNPuqScD6CounIpriGMZc1OnbW71V\n2r3abSV09FrfzvcPrCT0R+k3g6fxRd3/AIQsfiysc/je31LU9S0+xkWNbiKyh0+QRwlEACK9wFaN\nD2U446c58MfAOp+HLb4e+FPEtqP7U1bxHe6/dQqVlljj0i1HlxSBScMZGzsPILAEZ4H5/wDmyGTz\ni58zOd2ec+ufWus8IeM9T8FzarcaXFFJLq2nXOmu8oYmKO6AV3j2suH2ggE5AyeK6aHElNzi6kHo\n927u3u3vpq2o2v0T20KjiVdXX9aH0P8AGTXPEp+Fdjo3xKjhtPEt9rcl/Z6ckccT6fpohKFDHGB5\naySMNqtyQu45OTXyTTmdnYu5LMxySeSTTa+ezHHPEVOd9ktXd6d3/XY56lTmdz2/wf8As6fF3x54\ndtPFfhfRUu9MvvM8mU3VvGW8qRo2+V5FYYZSOR79K6b/AIZF+Pv/AELqf+Btp/8AHa/Q/wDZM/5N\n+8K/9v3/AKWz157oVtdajpcE3hew1Y+Kv7Wlxfr9oWyESXbbhI7N5LIIhtKgE549a/QKfCGD+r0a\nkuZucVLRrstEuV3eui/E7JYeCjF66r+uh8Zf8Mi/H3/oXU/8DbT/AOO0f8Mi/H3/AKF1P/A20/8A\njtfot/wmGpaNNqsfhvT7RLzUPFZ0wmd5jG5ktg/mt8zENlQCFAXHQA81LqXxX8R+GYtb0jxFaWcm\ns6dPZRW8lv5q2sq3+djOp3SDy9pLAEk9B610/wCqOVJXnOa36q3V/wAvZFexordv+vkfnA/7JPx5\njwZPD8SbiFGb60GSegH73rUn/DIvx9/6F1P/AANtP/jtffeqeMdV8Tx6bY6ssMj6d4k0Upc20U0M\nEyTSE8LPlgyFSG5PY963x4wvtCXXZdA062F3c+Ko9NIlkl2SNOkY8xzlypyQDtG0AcLnrC4Syxtv\nmnb1Xn/d8hKjR87f15Hp3w40jUPD/wAPPC+g6tH5N9pulWVtOgYNslhgRHXcpIOGBGQSD2rnvi38\nJPDPxf8ADL6DryeVcxbns7xFBltpSOo6ZU8B0Jww9CARyWqfFTxP4X/tvSvENrZTanZSWEdq9v5q\n27G/LAGQMWfEewk7eW6ACu1+HnjHVfE82rWOrLDI+nNCUubaKaGCZJlJ4WfLBkKkNyex7199DE4W\ntFYOSumrWflda/c/6sehGvTl+7/r+tD8VPiJ8O/E3ww8TXHhbxRb+VcRfNHIuTFPET8skbHqp/MH\nIIBBFcNX7ufFz4R+Gfi/4ZfQtdTyrmLc9neIoMttKR1Hqp4DoThh6EAj8XPiJ8O/E3ww8TXHhbxT\nb+VcRfNHIuTFPET8skbd1P5g5BAIIr8a4p4WqYCpzw1pvZ9vJ/1qeVisK6butj77/wCCb3/H38R/\n9zRf531fqJX5d/8ABN7/AI+/iP8A7mi/zvq/USv4m8SP+R1X/wC3f/SIn53nP+8y+X5IK+WP2oLL\nVrCLwH8SbGwn1Sx8D6/BqGo29shllFmyNHJMsY5YxZDYHQZJ4BI+p6K+Ty7GfV60atr26d01Z/gz\ngo1eSSkfAS/F7TfGHx68Fr8IviRfeIbDxBLqDahpRVfsdlHFZMYDsMCSIDKMne5OfSvnj4Q6L4d1\nPS/CfhLxj400rw14x0TxHFdXOny6FKdfbUUvGba98LjMomBGX2FVUqD92v2ASGKNnaNFVpDliAAS\nfU+tJ5MPnfaPLXzcbd+Bux6Z64r6alxVClB06NNpWSveN7rm39yz0lbZPRanfHMVFcsY2+6/Xy8z\n4f8AhKnw38G/tJfEjwrq9jY6Xr2oajZz6FEbVUkaBrHMrWrhMKv3t+0jnIPNbn7THinQPBXxR+C/\nijxTepp+lWGp6m088gJWNWtAgJ2gnliB0r7IwM7sc9M0tea88i8THETg37vK1zb+5yXTtp3tZ6mH\n1tOoptdLb+Vux8K/F39o/wCGvia18OweCrvQNasptRMF3rOs2cl3YaMzQu0TtGfLbzJtrLGQwHB3\nHFfPEOkReKv2cPjymm21nq/2TXFvbJrCxe2t03JavLNaW8pd4VaME8Mcr0JUiv1ueNJFKSKHU9QR\nkU+u7B8TUsNCMKFJqzT1lfaSl/KvTSyt0ve+tLHxgkoR6rr537H5c/EvxB8PtQ+IPw98a+E9T0XS\nvh6tjfWsN9c6O19pNvq7MjSCW3RoFSV49gEjdNp9CVlk8J/C7Rvh74e1W2+KWkx3Fl4g1fVNIubn\nSnXQne4SMXFn9lkeRRGm4GNw+V3HZkgkfp5JDDLEYJY1eNhgqQCpHpjpSvDFJH5MiKycfKQCOOnF\naLi20IQjFpR84u697e8Hr7zv0eumpSzGySSenp5+XmfmDfeIfCOr/wDCjfi18QvCel+HNBjvddtN\nTkisgNOdWhdLaUI0e/yZpd0kYkXgknP8R/TLSbjTrrSrO60cobCaGN7fy12p5LKCm1cDA24wMcCk\nutV0i1v7TSby8givdQ8w21vJIqyz+SA0hjQnc+wEFtoOBya0a8jN80WJjBKDja9tbqzk31W6u1e+\nttkc2JxHOlpa1/zbCuA+Jnwz8J/FnwndeEPF9r59rP8ANHIuBNbzAfLLE2Dtdc/QjIIKkg9/RXkU\nK86U1Upu0lqmjmhNxd1ufh5qulePv2S/H0nhnxNG+o+GdRcyQzRjEdzGMDzocnCToMCSMnngE42P\nX1dpGr6br2m2+saPcLdWd0oeORDww/mCDwQeQQQQCDX2t8TPhn4T+LPhO68IeL7Xz7Wf5o5FwJre\nYD5ZYmwdrrn6EZBBUkH8e9V0rx9+yX4+k8M+Jo31HwzqLmSGaMYjuYxgedDk4SdBgSRk88AnGx6/\nvTwB8flJRyvNJej7ea8u8em60uj1akI4uPNHSovx/wCCfX9FZ2kavpuvabb6xo9wt1Z3Sh45EPDD\n+YIPBB5BBBAINaNf3JTqRnFSi7p7M8Fpp2YUUUVYgooooA+ML3/hP7ey+J+oeFLyJLCLV7kXcAg8\ny6aIogleJy20FYzkAr2ODnFdj4RufDGgfEvw/DpV+q6G3g9UtZpnCLKVugxJJwN+Msw4xzwK+kLX\nTNNsWuXsrSG3a8kMs5jjVDLIwwXfA+ZiBgk81g3HgPwRdWtrY3Hh/T5LayLNBEbWLZEXOW2LtwMn\nk4HJ5NfAU+EK1KcatOom07+9ey99ysuyaeum6Xmeu8xjJOMlp5W7W/r1Pk7TkttfsvD9rJO81lf+\nM9RQtDMy+ZC6twHjIO1h6HkGvYfh9o+n+FPiv4t8NaBGbXS/sdjcrbhmZFlfeGZQxON3f1/KvYYv\nDPhyAQiDSrSMW87XMe2CMbJ3+9KuBw57sOT61ej03Tor6bVIrWJLy4VUknVFEron3VZwNxAzwCeK\n2yvhGVCdOrKSc4yTvbWyg4tX7Nu9iK+YqScUtGv1uXaKKK+5PLCiiigAqOWKKeJ4J0EkcgKsrDKs\np4IIPBBFSUUmr6AfDfxV+FWp/DnU4/HXgWSW3sLeZZ1aBmWbT5lbKujDkIG+62cqeDxg1+j/AOy3\n+1JYfGCwj8IeMJI7PxpZxk8YSPUY0GTLEBgCQAZkjHu6DbuWPgJYop4ngnQSRyAqysMqynggg8EE\nV8P/ABV+FWqfDnVI/HXgWSW30+3lWdWgZlm0+ZWyrKw+YIG+63VTweMGv5I8cvA2hjaEsZg42tro\ntYvuu8X1XTdabfRYXFRxMFQrP3uj/r+vmfvRRXxt+y3+1JYfGCwj8IeL5I7PxpZxk8AJHqMaDJli\nAwBIAMyRj3dBt3LH9k1/nRmuVV8FXlh8RG0l+Pmu6Z5dehKlJwmtQooorzjEK8b8c/H74T/DfxCP\nCvjDWJbTVfsy3hhisby6It3ZkEjNbwyKBuUjk5GOeor2SvhLx7rPxH0H9qjXtT+Geh2niDUIPBED\nyW11cPbkxpeSsBFsRt8hYABSVBz94V7WR5fTxFSUamyi3uo9V1aaW/Y68JRjOTUu3e34n2L4O8ae\nFfiBoFv4o8GanFq2l3WRHPCTjKnDKwIDKwPVWAI7iuor8y/Bup6h4b+EPw58ZeH9f8u5+InxMsb/\nAFhbIvbQxHUJHW6sGQnPloYwrK3BIzyMGvQ/jd448Y6L45+K9lpGt3lnb6V4DivrSOKZ0WC6Nw6m\naMA4WQgAbhzivUr8Lt4h0qU9LtK++klHX5v/AIY3ll/v8sX3/Ox941wPxB+J/gj4W6fZap451BrC\n31G5W0tylvPdPJOys4QJbxyPkqpOcY49cV8nwWvi3wH46+DGvx+Ndc1o+OHe31W11G7860l8yyMy\ntHAqpHEUc5Xao7Zzzntv2rBrhuvhOPDLWy6qfGFn9mN4Ha3Ev2efb5gjIcrnrtINc9DIqf1mlSnO\n8ZJu6025k90+sd7bERwi9pGLd07/AIX/AMj3T4f/ABY+HvxRivZPA2sJqLaa6x3URjlt54GbO3zI\nZ0jkUHBwSuDg46GvRK/ORfEN78OPi18ULz476mmn6xrHhe31BNS8Ms0aQafBN9kWOOOZWkS6aVl2\nO7Mp4AI6VpfDa58U/wDC40+HrX3jTSNC8U+HLq52+IdQjuL1JopowlzaSJLOYWKMQysBgnp0x1Yr\nhiPvVKU7QSUlfW+icrPS9vT1saVMAtZRelr/AIa/1Y+89E17RfEunLq3h+9h1Cyd5I1mgcPGXhcx\nyKGHGVdSp9CCK5z4h/ELQvhn4fXxL4iSZ7R7q2tALdA7+ZdSCJOGZRjcwyc8Cvzp+H194g+Fn7HG\nofFDwv4h1EarezT2KLdXHn2Nh52rtbtcxwMNqSbSWLc5c5I5r1L9oD4YTeBvglNEvjPX/EU2o6to\nimfVr0XnlOLxf3sCsm1M7vu8rgDjrnT/AFaowxapSqXi6jgtHd2av6WuvXsP6hFVOVy0vb7tz79o\nr4/8O6f4g+G/7S2leC4PFms6/o3iLw/d3k8Gr3hu9l1bTooliyFEeVOCqgLyeOmPsCvmsfglRcbS\n5lJXT27r80cNalyWs731CiiiuAxCue8VeFfD/jfw/e+FvFNlHqGmahGY5oZBwR1BBHKsp5VhgggE\nEEV0NFXTqShJSi7NDTad0fgt+0V+zr4g+BXiAOhk1DwtqEhFjfEcqevkT4GFlUdDwHAyMEMq/OFf\n0r+KvCvh/wAb+H73wt4pso9Q0zUIzHNDIOCOoII5VlPKsMEEAggivw3/AGiv2dfEHwK8QB0MmoeF\ntQkIsb4jlT18ifAwsqjoeA4GRghlX+j+AuPY46KwmLdqq2f83/B7rruj7HK80VVck/i/M+cK+pPh\nz+yb48+Jng3T/G+i6ppttZ6l5vlx3DzCVfJleFtwSJl5ZCRgnjFfLdftJ+yZ/wAm/eFf+37/ANLZ\n6/oDgzJ6GNxUqVdaKLe9tbpfqfTYKjGc2pHxt/wwl8UP+g3o3/fy4/8AjFH/AAwl8UP+g3o3/fy4\n/wDjFfQ/gq2ur/R/DV14WsNWXxD9v3XN+ftC2LWyzv5gd3bynXyxtwozkY69fRtK8Yatp2mWVn4W\n0+zhuNX1/UbPE7zNECjSsZCdzNklckDjsNvUfXYbhzLJpSlTaTV9JN7202WuuvbzNacKTV3H8T4y\n/wCGEvih/wBBvRv+/lx/8YpjfsLfE1CqvruiqXOFBluBk+g/cV9oaj8XfEWhNfeHNZtrIa/a39va\nCaMTGz8q5iMyzFBul+VVIKA5zjBxVIeK9R8U6v4SbVI4xcWGvSQebBHLFDOn2R3WRFl+cfewQScE\nda1lw5k9+WMZXva133s18inChslr/wAE+P8A/hhL4of9BvRv+/lx/wDGK/SrwJoN14W8D+HvDF86\nS3OkadaWcrxklGe3hWNipIB2krxkA47V5HY+MNW0yWew8MadZx3Op+J9RsW8+SbyyyIX81jlyCSo\nyqgL2AXORPf/ABX8Q6Fp+p2WtW1m2s2eq2+mRyRCUWp+0xecsrrlpMKgYsoJJOAK9nJ8Nl+AcqlJ\nNX0e7Xe3rY3ozpU7tHp3jzwH4a+JHhq58LeKbYXFpcDKsOJIZB92SNv4XXse/IIIJB/F74x/BzxL\n8HPEraPq6m4sLgs1lequI7iMfntdcjemeOoyCCf2N+Hni7U/E8epwaqkTTadOqLcW8UsUE8ciBgy\nLNlwQcqwyeR71qePfAXhr4keGrnwt4pthcWlwMqw4khkH3ZI252uueD35BBBIq+IOHqOaUFVpaTW\nz7+T8vy6GlahGtHmjufgDRXrvxj+DniX4OeJW0fWFNxYXBZrK9VcR3EY/Pa65G9M8dR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RNH1OPWbKyneKK8hBCTIp4YA/kcZGfukjBP2X+07+063idrr4d/Du6K6MpMd9fRnBuyODFE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\noUt1ICjGcYHSuiP8KXqvyZS2OMooo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    }
   },
   "cell_type": "markdown",
   "id": "791421a9-75e6-49ca-af7b-6250b4ca7890",
   "metadata": {},
   "source": [
    "![AIM321-Demo-Scenario-Overview-1.jpg](attachment:b9cf6072-691a-4a32-b60d-71ec11c805f3.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6967670f-22f7-40c1-aed6-708ce81bf0f0",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Uncomment if not using latest version of SageMaker SDK\n",
    "#import sys\n",
    "\n",
    "#! pip install --upgrade pip\n",
    "#!{sys.executable} -m pip install sagemaker==2.114.0\n",
    "#!{sys.executable} -m pip install -U boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3acef961-5c67-43c5-84cc-504a27a2207a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# If you run this notebook in SageMaker Studio, you need to make sure latest python SDK is installed and restart the kernel, \n",
    "# so please uncomment the code below, and run it.\n",
    "#import IPython\n",
    "#IPython.Application.instance().kernel.do_shutdown(True)  # has to restart kernel so changes are used"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c44bed-5dc1-452f-b383-2a7366ccc8e4",
   "metadata": {},
   "source": [
    "# Setup\n",
    "\n",
    "In this section, we'll import libraries and setup variables needed to configure pipeline steps and construct the model build pipeline. We'll also upload the scripts that will be used for the data preprocessing and model evaluation steps to S3.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eaaadc74-5506-4185-bd91-1dcb2b5987a6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "from time import gmtime, strftime\n",
    "import sagemaker\n",
    "import json\n",
    "import boto3\n",
    "import pandas as pd\n",
    "\n",
    "# SageMaker Job Imports (ex. Processing, Training) \n",
    "from sagemaker.sklearn.processing import SKLearnProcessor\n",
    "from sagemaker.processing import ProcessingInput, ProcessingOutput, ScriptProcessor\n",
    "\n",
    "from sagemaker.model import Model\n",
    "from sagemaker.inputs import TrainingInput\n",
    "\n",
    "from sagemaker.model_monitor import DatasetFormat, model_monitoring\n",
    "from sagemaker.clarify import (\n",
    "    BiasConfig,\n",
    "    DataConfig,\n",
    "    ModelConfig\n",
    ")\n",
    "\n",
    "from sagemaker.model_metrics import ModelMetrics, MetricsSource\n",
    "from sagemaker.drift_check_baselines import DriftCheckBaselines\n",
    "\n",
    "\n",
    "# SageMaker Pipeline Imports \n",
    "from sagemaker.workflow.properties import PropertyFile\n",
    "from sagemaker.workflow.conditions import ConditionGreaterThanOrEqualTo\n",
    "from sagemaker.workflow.condition_step import ConditionStep\n",
    "from sagemaker.workflow.functions import JsonGet\n",
    "\n",
    "from sagemaker.workflow.pipeline import Pipeline\n",
    "from sagemaker.workflow.steps import ProcessingStep, TrainingStep, CreateModelStep, TransformStep\n",
    "from sagemaker.workflow.model_step import ModelStep\n",
    "\n",
    "from sagemaker.workflow.parameters import (\n",
    "    ParameterInteger,\n",
    "    ParameterString,\n",
    ")\n",
    "\n",
    "from sagemaker.workflow.functions import Join\n",
    "from sagemaker.workflow.execution_variables import ExecutionVariables\n",
    "\n",
    "from sagemaker.workflow.pipeline_context import PipelineSession\n",
    "\n",
    "from sagemaker.workflow.check_job_config import CheckJobConfig\n",
    "from sagemaker.workflow.clarify_check_step import (\n",
    "    DataBiasCheckConfig,\n",
    "    ClarifyCheckStep,\n",
    ")\n",
    "from sagemaker.workflow.quality_check_step import (\n",
    "    DataQualityCheckConfig,\n",
    "    QualityCheckStep,\n",
    ")\n",
    "\n",
    "from sagemaker.workflow.parameters import (\n",
    "    ParameterBoolean,\n",
    "    ParameterInteger,\n",
    "    ParameterString,\n",
    ")\n",
    "\n",
    "from sagemaker.workflow.steps import CacheConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62be9dbd-4467-400a-b6d7-89bbf7cadb1d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# SageMaker variables\n",
    "session = PipelineSession()\n",
    "bucket = session.default_bucket()\n",
    "role_arn= sagemaker.get_execution_role()\n",
    "region = session.boto_region_name\n",
    "sagemaker_client = boto3.client('sagemaker')\n",
    "aws_account_id = boto3.client('sts').get_caller_identity().get('Account')\n",
    "\n",
    "print(\"RoleArn: {}\".format(role_arn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c411aa33-dabf-436a-af20-c49e8e5c75ee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Setup S3 paths for pipeline inputs, outputs, & artifacts\n",
    "print(\"Demo Bucket: {}\".format(bucket))\n",
    "bucket_prefix = f\"aim321-demo-model-monitor-batch-transform/{int(time.time())}\"\n",
    "model_artifacts = \"s3://{}/{}/model-artifacts\".format(bucket,bucket_prefix)\n",
    "transformed_data_path = \"s3://{}/{}/transformed-data\".format(bucket, bucket_prefix)\n",
    "raw_input_data = \"s3://{}/aim321-demo-model-monitor-batch-transform/raw-customer-data/20221122/churn-dataset.csv\".format(bucket) \n",
    "\n",
    "print(\"Raw Data Path: {}\".format(raw_input_data))\n",
    "print(\"Transformed Data Path: {}\".format(transformed_data_path))\n",
    "print(\"Model Artifact Path: {}\".format(model_artifacts))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95220e6f-f5f0-4bf5-b4ba-1fbf60290daf",
   "metadata": {},
   "source": [
    "### SageMaker Model Registry\n",
    "\n",
    "Specify the model package group for registering high performing model versions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04da14f4-c5ac-438e-a0da-915514ae05d6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_package_group_name=\"customer-churn\"\n",
    "\n",
    "print(\"SageMaker Model Registry - Model Package Group:\", model_package_group_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b53bb502-1354-4f58-94f4-c3a4278a2452",
   "metadata": {},
   "source": [
    "### Setup Step Caching Configuration\n",
    "\n",
    "This configuration can be enabled on pipeline steps to allow SageMaker Pipelines to automatically check if a previous (successful) run of a pipeline step with the same values for specific parameters is found. If it is found, Pipelines propogates the results of that step to the next step without re-running the step saving both time and compute costs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1226b10d-6297-4fd7-b873-84a6227a123f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cache_config = CacheConfig(enable_caching=True, expire_after=\"PT12H\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5363796e-1d3a-4a62-944f-4320290acf9e",
   "metadata": {},
   "source": [
    "### Setup Runtime Parameters\n",
    "\n",
    "Configurable parameters that can be passed in at runtime without changing pipeline code. \n",
    "\n",
    "            pipeline.start(parameters=dict(...))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01e0b1bd-0343-449c-ad5a-3a16c635c593",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Setup Runtime Parameters \n",
    "\n",
    "# Data Preparation Step - S3 URI to input data\n",
    "input_data = ParameterString(\n",
    "    name=\"InputData\", \n",
    "    default_value=raw_input_data\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f2ae01e-f568-4dfc-9e23-361887e96210",
   "metadata": {},
   "source": [
    "Train and baseline pipeline is part 1 of the batch inference with data quality model monitoring using SageMaker Pipelines. The necessary steps and code are within 1.SageMakerPipeline-BaselineData-Train.ipynb. Train and baseline pipeline takes the raw customer churn data as input, and generates baseline and the trained model which will be registered with the model registry for subsequent use with Batch Transform.\n",
    "\n",
    "1-A. Configure Model Build Pipeline\n",
    "1. Prepare Data Step\n",
    "2. Train Model Step\n",
    "3. Evaluate Model Step\n",
    "\n",
    "1-B. Configure Steps to Baseline, Package & Register Model Version\n",
    "1. Baseline Step for Data Drift Model Monitor\n",
    "2. Package Model Step\n",
    "3. Register Model Step\n",
    "\n",
    "1-C. Conditional Step: Configure step to evaluate model performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ff366b-54a5-4ede-809f-7f7fbbcc6272",
   "metadata": {
    "tags": []
   },
   "source": [
    "--- \n",
    "### 1-A Configure Model Build & Baseline Pipeline Steps\n",
    "\n",
    "Model build pipeline is a three step process, \n",
    "\n",
    "1. Prepare Data Step\n",
    "2. Train Model Step\n",
    "3. Evaluate Model Step\n"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "id": "24364524-e527-4f0e-88a6-8a97c3984bce",
   "metadata": {},
   "source": [
    "![AIM321-Model-Build-1.jpg](attachment:d9e235c0-7a2e-4e7a-8088-050b0e8f9712.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a8e1790-0fd8-4243-82dc-fc6259f5bc4b",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 1. Prepare Data Step: Configure Processing Job & Pipeline Step\n",
    "\n",
    "Prepare data step is the first part of the three parts of model build pipeline which involves pre-processing the raw data and preparing it for training and evaluation. For processing, prepare data step uses SKLearnProcessor container. It allows to select the appropriate instance type and instance count for processing the data. Depending on the size of the data, the instance type can be changed to smaller or larger instance type. Prepare data is a [ProcessingStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.steps.ProcessingStep) and utilizes *preprocessing.py* script. It reads the raw data from designated S3 bucket as input using ProcessingInput, does the necessary data processing as per the logic within *preprocessing.py*. Since *preprocessing.py* has logic for converting categorical variables from the raw data into dummy variables, this logic substantially increases number of columns as feature engineering.  It also splits the data into 3 parts for training, validation, testing. Prepare data step then writes the 3 datasets as output using ProcessingOutput to designated folders in S3. These 3 datasets are inputs for train model step and Evaluate Model Step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a74b94b-c799-440e-bafe-2a2b18698483",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Configure SageMaker Processing Job\n",
    "preprocess_data_processor = SKLearnProcessor(\n",
    "    framework_version='1.0-1',\n",
    "    role=role_arn,\n",
    "    instance_type='ml.m5.xlarge',\n",
    "    instance_count=1,\n",
    "    base_job_name='aim321-data-preparation',\n",
    ")\n",
    "# Configure Pipeline Step - 'ProcessingStep'\n",
    "data_preparation_step = ProcessingStep(\n",
    "    name='PrepareData',\n",
    "    processor=preprocess_data_processor,\n",
    "    inputs=[\n",
    "        ProcessingInput(\n",
    "            source=input_data,\n",
    "            destination='/opt/ml/processing/input'\n",
    "        ),\n",
    "    ],\n",
    "    outputs=[\n",
    "        ProcessingOutput(\n",
    "            output_name='train',\n",
    "            destination=f'{transformed_data_path}/train',\n",
    "            source='/opt/ml/processing/train'\n",
    "        ),\n",
    "        ProcessingOutput(\n",
    "            output_name='validation',\n",
    "            destination=f'{transformed_data_path}/validation',\n",
    "            source='/opt/ml/processing/validation'\n",
    "        ),\n",
    "        ProcessingOutput(\n",
    "            output_name='test',\n",
    "            destination=f'{transformed_data_path}/test',\n",
    "            source='/opt/ml/processing/test'\n",
    "        )\n",
    "    ],\n",
    "    code=\"s3://{}/aim321-demo-model-monitor-batch-transform/scripts/preprocessing.py\".format(bucket),\n",
    "    cache_config=cache_config\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8412cc0-ef61-4861-a583-5203de451bbc",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 2. Train Model Step: Configure Training Job & Pipeline Step\n",
    "\n",
    "Train model step is the second part of the three parts of model build pipeline which involves training the appropriate model. Based on the target variable of the dataset, it is a binary classification problem. XGBoost model is selected for this, for which xgboost container is utilized for training. Train model step is [TrainingStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.steps.TrainingStep) within pipeline and takes train and validation data from prepare data set as part of TrainingInput. Train model step trains the xgboost model and saves the model in S3 bucket as *model.tar.gz* file. *model.tar.gz* file is an essential file which will be utilized as next steps for evaluate model step and for model registry later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9c723a9-f4da-4459-8d80-83a694b8b69e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Configure SageMaker Training Job\n",
    "\n",
    "xgboost_container = sagemaker.image_uris.retrieve(framework=\"xgboost\",region=region,version=\"1.0-1\",py_version=\"py3\",)\n",
    "estimator = sagemaker.estimator.Estimator(\n",
    "    image_uri=xgboost_container, \n",
    "    role=role_arn,\n",
    "    output_path=f'{model_artifacts}',\n",
    "    instance_count=1, \n",
    "    instance_type='ml.m5.2xlarge', \n",
    "    volume_size=5, # 5 GB \n",
    ")\n",
    "\n",
    "# Tuned hyperparameters\n",
    "estimator.set_hyperparameters(\n",
    "    objective=\"binary:logistic\",\n",
    "    num_round=48,\n",
    "    max_depth=5,\n",
    "    eta=0.2,\n",
    "    gamma=4,\n",
    "    min_child_weight=6,\n",
    "    subsample=0.7,\n",
    "    silent=0,\n",
    ")\n",
    "# Configure Pipeline Step - 'TrainingStep'\n",
    "training_step = TrainingStep(\n",
    "    name='TrainModel',\n",
    "    estimator=estimator,\n",
    "    inputs={\n",
    "        'train': TrainingInput(\n",
    "            s3_data=data_preparation_step.properties.ProcessingOutputConfig.Outputs[\n",
    "                'train'\n",
    "            ].S3Output.S3Uri,\n",
    "            content_type='text/csv'\n",
    "        ),\n",
    "        'validation': TrainingInput(\n",
    "            s3_data=data_preparation_step.properties.ProcessingOutputConfig.Outputs[\n",
    "                'validation'\n",
    "            ].S3Output.S3Uri,\n",
    "            content_type='text/csv'\n",
    "        )\n",
    "    },\n",
    "    cache_config=cache_config\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e44569-6c9d-4f8a-a93f-8768e95b9932",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 3. Evaluate Model Step: Configure Processing Job & Pipeline Step\n",
    "\n",
    "Evaluate model step is the last step of the of the three parts of model build pipeline and involves evaluating the trained model with test data. For processing, evaluate model step uses [ScriptProcessor](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.processing.ScriptProcessor) container. It allows to select the appropriate instance type and instance count for processing the data. Evaluate model step is a [ProcessingStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.steps.ProcessingStep) and utilizes *evaluate.py* script. It unpickles and reads the trained model from *model.tar.gz* from S3 bucket, which was output of train model step. It utilizes the test data from from prepare data step to evaluate the model and generates performance metrics (accuracy, area under curve) as output. As part of ProcessingStep, evaluate model step uses ProcessingInput as input and ProcessingOutput for output. The evaluation metrics are saved as .json file which is required for later steps related to configuring baseline step and configuring conditional step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cadd6c93-34ee-4b7e-a374-33a71657289b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Configure SageMaker Processing Job\n",
    "evaluation_processor = ScriptProcessor(\n",
    "    image_uri=xgboost_container,\n",
    "    command=[\"python3\"],\n",
    "    role=role_arn,\n",
    "    instance_type='ml.m5.xlarge',\n",
    "    instance_count=1,\n",
    "    base_job_name='evaluation',\n",
    "    sagemaker_session=session,\n",
    ")\n",
    "# Specify where we'll store the model evaluation results so\n",
    "# that other steps can access those results\n",
    "evaluation_report = PropertyFile(\n",
    "    name='EvaluationReport',\n",
    "    output_name='evaluation',\n",
    "    path='evaluation.json',\n",
    ")\n",
    "\n",
    "# Configure Pipeline Step - 'ProcessingStep'\n",
    "evaluation_step = ProcessingStep(\n",
    "    name='EvaluateModel',\n",
    "    processor=evaluation_processor,\n",
    "    inputs=[\n",
    "        ProcessingInput(\n",
    "            source=training_step.properties.ModelArtifacts.S3ModelArtifacts,\n",
    "            destination='/opt/ml/processing/model',\n",
    "        ),\n",
    "        ProcessingInput(\n",
    "            source=data_preparation_step.properties.ProcessingOutputConfig.Outputs['test'].S3Output.S3Uri,\n",
    "            destination='/opt/ml/processing/test',\n",
    "        ),\n",
    "    ],\n",
    "    outputs=[\n",
    "        ProcessingOutput(\n",
    "            output_name='evaluation', \n",
    "            source='/opt/ml/processing/evaluation',\n",
    "        ),\n",
    "    ],\n",
    "    code=\"s3://{}/aim321-demo-model-monitor-batch-transform/scripts/evaluation.py\".format(bucket),\n",
    "    property_files=[evaluation_report],\n",
    "    cache_config=cache_config\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff2961a-361f-43ac-9ccc-f637aa25674b",
   "metadata": {},
   "source": [
    "## 1-B. Configure Baseline Step for Data Drift Model Monitor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495f83bc-8edf-454d-9b53-d86009357bbe",
   "metadata": {
    "tags": []
   },
   "source": [
    "### ----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2561dd-dd4b-4aca-86c9-494048e9deda",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "![Step5](./images/AIM321-Baseline-Data-1.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57850746-efbf-4829-81f8-281db44e4fd1",
   "metadata": {},
   "source": [
    "In order to monitor the model and data, a baseline is required. Baseline step sets the required baseline for data quality using [QualityCheckStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.quality_check_step.QualityCheckStep). Data Quality Baseline Job has two key parameters - data_quality_check_config and check_job_config. check_job_config allows to select the appropriate instance type and instance count for processing data quality check using CheckJobConfig. data_quality_check_config  sets the input path for the training data from previous prepare data step as well as path for output within S3. As output, QualityCheckStep generates two .json files, statistics.json and constraints.json which are saved in S3 buckets. statistics.json contains statistics (like mean, sum, standard deviation, min, max) of each of the feature columns of the training dataset. constraints.json contain information like completeness and non-negative or not for the feature columns of training dataset. Statistics and constraints form key components for setting baseline for data quality monitoring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ba5ee15-bfbe-4ce4-9683-2e7ecf55f36c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Configure the Data Quality Baseline Job\n",
    "\n",
    "# Configure the transient compute environment\n",
    "check_job_config = CheckJobConfig(\n",
    "    role=role_arn,\n",
    "    instance_count=1,\n",
    "    instance_type=\"ml.c5.xlarge\",\n",
    "    volume_size_in_gb=120,\n",
    "    sagemaker_session=session,\n",
    ")\n",
    "\n",
    "# Configure the data quality check input (training data), dataset format, and S3 output path\n",
    "data_quality_check_config = DataQualityCheckConfig(\n",
    "    baseline_dataset=data_preparation_step.properties.ProcessingOutputConfig.Outputs['train'].S3Output.S3Uri,\n",
    "    dataset_format=DatasetFormat.csv(header=False, output_columns_position=\"START\"),\n",
    "    output_s3_uri=Join(on='/', values=['s3:/', bucket, bucket_prefix, ExecutionVariables.PIPELINE_EXECUTION_ID, 'dataqualitycheckstep'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d95db497-b9b7-4e6e-aa82-e19f3a51fe12",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Configure Pipeline Step - 'QualityCheckStep'\n",
    "baseline_model_data_step = QualityCheckStep(\n",
    "        name=\"DataQualityCheckStep\",\n",
    "        # skip_check, indicates a baselining job\n",
    "        skip_check=True,\n",
    "        register_new_baseline=True,\n",
    "        quality_check_config=data_quality_check_config,\n",
    "        check_job_config=check_job_config,\n",
    "        model_package_group_name=model_package_group_name\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a82fcc0-c458-4a4d-a364-374724895fe9",
   "metadata": {},
   "source": [
    "## 1-B. Configure Steps to Package & Register Model Version\n",
    "\n",
    "Configure steps to package and register model version are broken down in two steps,\n",
    "\n",
    "1. Package Model Step\n",
    "2. Register Model Step\n"
   ]
  },
  {
   "attachments": {
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/AMPzO4gmyI5opongmjYjkb45GUEdCQe1WlpYTetz9Zv2wPiz8Q/HH7dmlfss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\nAIKW6r8J/hFB8FPid4DtfiL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2tfE3\niP4S+Gfi6+q/FDRdF/tyHSJtEltbeeIWyXRg+1efInmlHBBGRgjIzu28B4a/Y08IeOfgjrPxH8G+\nLtbuNa0Dw3L4juI7zw1c2WjyraRLLdWlvqMkmySWLcQrAYk2kqu3JX6R/aU/bA+EPw8+MHi3W/hP\n4Ksdd8e6n4etNIi8Z2+tvdWixXWnwpIyWUatB50K/usiQcr8w+8reWah/wAFF9L1H4fah4bufh1c\n/wBu6r4Mm8HS3UfiG4j0yGF7Y263EGleQbdGzhyPvcbVkAJJNR6EcP8AwT48G6j438J/CvRPi0Lv\nxr4ksbXV59O/sOcLYaVLam5lneZZmSSQEbI4lwzkjJQHjw79p/8AZTt/gR4X8J+O/D2q6rqeh+KJ\nry0Met6NNoV/bXNnt+9bTsXMcqtujbA4BB5rbtf23dc0n9o7wz+0FonhmOA6DotnoU+mS3bSJd21\nva/ZZT56xIY2kHzrhDsYDO8Ag+V/H34x/Dz4ox6ZbeAfCWq+GIrKWaWU6n4ju9d8zzQoVI1uFURL\nHg4IyzA/MeBhq4nY+baKKKokKkhmlt5Ungdo5Y2DKykhlYHIII5BB6Go6KGgPprw94h0j4r6Q/hj\nxOh/tfLSgxKDJLIR811arwDcEDM8HAuANy4mHzeD+J/DGpeFNS/s/UNsiSKJYJ4juhuIW+7LE3GV\nOPYggqwDAgYMM0tvKk8DtHLGwZWUkMrA5BBHIIPQ19LeHvEOkfFfSH8MeJ0P9r5aUGJQZJZCPmur\nVeAbggZng4FwBuXEw+b5+pTlgpOpTV6T3X8vmvL8vTbladN3W35HzLRXReJ/DGpeFNS/s/UNsiSK\nJYJ4juhuIW+7LE3GVOPYggqwDAgc7Xu06kZxUou6Z0ppq6CiiirGFFFFAH6r/Bj4teF9a/Za+L/x\nE1X4O/D261r4XReFrfTZJPD6P9o/tK7a0nkvCZC0shRAwZWT5ySc9K8Z8X/AX4V3fwh0H9o7x38Q\nLXwZqnxN/tK70zwxpWgTPbJLa3rW8kcLpOyxQIMN8wBG4KobFeCeAvjYfA/wR+KfwaGjfbf+FmNo\nZN99o8v7F/Y1091/qfLbzfN3bfvpsxn5uldFqH7R91deE/gj4ctvD8KTfBe4vLmOWabz4tRe61BL\n8LJCY18tVKBGG59wyeOlTYq59St+xV4a0DR/DHxY+H/iXUtZtLDxPoOn3dv4n8K3GmWtxHqN0sUU\n8UVxKPtUG/AkiBG5WwXXu/4g/sr/AAsbU/H/AMW/jJ8SLfwLpdp4/wBT8LraaL4cke3kmgRZ1a2h\njuWFuhVmOxywUL99mIFTfED/AIKMaL4u0/UrbS/hzfWlzrWv6N4gvJrzxNc36CbSbtbowW8E1uY7\neF9uxREF2g5IfCqPnT41ftVt8YPAfiDwR/wjA0ldd8d33jbz/tvnmL7bbfZ/sezyY92z73m5Gemw\ndaSuNtHmH7RfwZu/2fPjR4m+D97qS6w/h6WFVu0j8kTR3MEdzGxj3PtOyVcjccHPJrxOvdP2lPjU\nf2h/jX4i+MR0f+wD4g+x/wChfaPtXlfZLSG1/wBb5cW7d5W77gxnHOMnwuqRLCiiimIK9J+HnxDm\n8GzXOmapbDVvDerAR6hp8h+SVOzof4JU6qwwcgexHm1FYYnDQrQdOorp/wBff2fQmcFJWZ6n8Q/h\n5D4chtvFXhW5OreEtWJ+yXePnifqbe4A+5Kn4BgMjuB5ZXpPw8+Ic3g2a50zVLYat4b1YCPUNPkP\nySp2dD/BKnVWGDkD2ItfEP4eQ+HIbbxV4VuTq3hLVifsl3j54n6m3uAPuSp+AYDI7gefhsTOlNYf\nEO9/hl/N5P8AvfnuuqWUJuL5J/J9/wDgnllFFFeubhRRRQAV+sn7Kngizuv2ONT8b+H/AAj4B1rx\nYvjyXTjd+OorUW66d/ZcMxiimuHi+cS4ZUD9DIdvUj8m6+w/hT+0j8NPCvwGuvgH8VPhlL470mbx\nK3iWKaHXZNIaO4NmlmqYitpSwVFc53gHd935QSpDiei+Nv2YNb+Ks3hj4h+FLjRIdR8YeMn8F6tp\nPhyzWLR9F1CEKkMls8U8qzQTQKbhnGxck4JByOh+HH7EXwp+IfjrxB8O9L+JGu32oaZrdzokN5Ye\nErm601HgYRrPdXUc7RRI8m4D58BQGLANxxPwb/bXsvgBqfxET4WeBBY+H/F0ET6Pp93qZvX0LU4L\ndoEvo7iS33TNmR2K7YyVKpvwuT0nwE/b0sPg/wDDfwn4G8QeBbrxDceCdUuNTsLiz1+50iK4e7l8\n1xfQwROLpkOQhlLLg4KlQQ06laHqsH7NUPxK+G37NnwGv76HQNU1LxB490291OG2W5YPpk5bld0R\nkXMJVcuMA59j8e+Bv2dND8SfCK0+L3iHxXNo9hJ43i8ITxQaY99JHHJZm7+1IkUoeV8jYIVXJJzv\n7V7X4b/b6j8P3Pg/V38AJdax4H8Va34g064/tNkUWniCSV7yykjEBDMRLhJ+Nu0Hy+oOzo37evw8\n+HVv4e0n4NfBuPw7pmheLB4rkiutbmvzdzNYz2MiHzYP3TFJgY3QkRlFOxjk0ahod74W/YQ+Fej/\nABA+E+v674o1i78G+LdeGkzWGteHbnSb2e8i2TR2xiedXW3uo9488cR7eNxPy8XpX7EPw6+IXjn4\nlax4H8XavceAvAt2trMuneHbm/1T7fcXM0f2C2tI5pHljt0RS9yWwQw+X7xGT43/AG/bHVj4GTwV\n4Fu9NHgjxanitZdW8Q3OtTXkgTa8DvcRBo1fJwUbao6Juyxh0P8Abg+Huh6x490yx+Ft1ZeB/iOY\nLzU9NtvElzFfRatDcSTteW19HCjor71UwbNuEHzYLKTUND5c/aT+B17+zx8WtT+Gl1ftqsFvDbXV\nrdvA1q89tdwrKheByzROu4o6MchlPbFeD16V8WvGug/EDxvd+JfDOj3WhadMkUcVpeanNq86eUgU\nlrq4Cu+4gtjAC5woAxXmtUiWFFFFMQV3/hrxLp0mnHwf4wDy6HK5eGZBun0+dus0I4yh482LOHHI\nw4BrgKKxr0I1I8sv+G80TKKaOh8S+GtR8LaiLG+KSxyoJbe4iO6C5gb7ssTfxKfzBBVgGBA56u/8\nNeJdOk04+D/GAeXQ5XLwzIN0+nzt1mhHGUPHmxZw45GHANYPiXw1qPhbURY3xSWOVBLb3ER3QXMD\nfdlib+JT+YIKsAwIGNCvJS9lV+L8/NfqunoTGTvZ7nPUUUV2GgUUUUAFFFFABRRRQAUUUUAFFFFA\nBRRRQAUUUUAFFFFAHb6XqljrljF4b8SSiEwjbY3zcm3J58qXHJgJ+pjPI4yp5fVNLvtGvpdN1KIw\n3EJwynkYPIII4KkcgjII5HFUK7fS9UsdcsYvDfiSUQmEbbG+bk25PPlS45MBP1MZ5HGVPq06kcTF\nU6jtNbPv5P8AR9NnpZxwacHdbf1/X9a8RRV/VNLvtGvpdN1KIw3EJwynkYPIII4KkcgjII5HFUK8\n2pTlGTjJWaNk01dBRRRUDP10/Zs/Zh+G/j/4EfDTxLc/B2/+IWqeMdb1HTtY1Sz1e5sP7ItIZ1jj\nuSgJgO1WJwyjdsIySePm+8/ZK8Ha38ZvHHw88N/FTw74dtNC8RT6FpcOv3EyX144fbHhYIGUqGPl\nmX5QWBIXGK6j4Z/tDfs+23wJ8A/DX4l3HjjTNY8B6rqWpxyeF/sUMVyb2RXjVp57hZE27RkiLIyc\nZr6I8Mf8FEvg9eeIvGnjPxBoeueCNc17xLb6wl14bttNurq9061tILWPT7qa+/1YZofNcxDlicYJ\nO7PUvQ+Qx+w3470fQdb8RfEfxh4b8C2ula1e+H4W1a6uFS8v7E/vPLeG3lCxMAdjybS390A5p3hv\n9hf4ieIvBOleIW8TaBp/iPxFok3iLSPDFxcyjWL/AEyKNpRNHGsTRgyIjNEpf5gDkrhsfUXhH9uX\n4GaH47+IHjnU7nxlqOjeMdU1G9uPB9zZ6Xc6HqMV0uyATCaV2t5AArStHu3bQMsowaX/AA3t4H17\n4TeFtEu/EnjrwJ4g8LaFFobaf4cXT5dLvltYzHBOZLpvNRiuBLx0GFBxmndhZGR4i+BvgGbx3oOm\n6J4G0vUoT8DbbxNcW897c6ag1EW7PJfq9tHKZbgEZEbhUc/eZcA15PoX/BPP4ga/baBa23j7wini\nXxboUOv6PoDX0/8Aad3azW5udvlfZ8IwQMMk7SythsKWHcRfthfBgXOgeJn0zX01y3+FN18PtQhE\nNq9qLtYEitLiBzOrmJmMhm3KGUbNqsd1ZHhj9sT4baN+0x8FPjPd6XqzaL8N/Btn4d1GFIrc3Ut3\nb6dd2jvbqZwjReZOpBd0YqCSoOATUNDzf4f/ALDPxD8feEPD+vJ4n0DRtc8Z2d1f+HvD99cyrqmq\nW1orOzxRpE6KHVGMe5xuxk4HNfE5BUlWGCOCDX60eBv28fBCfBfwJ4M8ReJ/HfgnXPAunjS3j8Kr\np0lnqVvF8sEjPeHfFKEADHaRwcBsgD8mHd5XaSQlmckknqSeppq/UTt0G0UUVRJ6T4D8eT+G50sb\n5y1gxcKxQS+R5o2v+7biSKQcSxHhxyMMARqePPAcFtA/inwsgbTGCPcW6OZfsvmnCSRueZLaQ8Ry\nHlT+7kw4+byKvSfAfjyfw3OljfOWsGLhWKCXyPNG1/3bcSRSDiWI8OORhgCOmUVXSTdprRN7Nfyy\n8v5Zbxf926OWpTcXzw+a/r+v182or13x54DgtoH8U+FkDaYwR7i3RzL9l804SSNzzJbSHiOQ8qf3\ncmHHzeRVyRk7uMlZrRp7p9n/AFZrVaG9OopK6N/wnBDdeKdGtrlFlilvbdHRhlWVpFBBB6gjrX7z\nfHb4J+FtPHxytfiX8IPCHgX4Z6Dod1P4T8Safb2+napPqyRRm1hXypt83mSFwVMSgkBed1fgZo2o\n/wBkaxY6t5fm/Yp4p9mdu7y2DYzg4zjGcGvVfjx8X2+Nfxq8R/GFNJXRm1+7juhYvKLtYjHGibTI\nY4xIDsycoOuMUNGqeh9T6l+xH4f1f4K6x8T/AIYeJ9d1i+0KHT53h1Xw1caPZXyX0yQEWV1PIVkM\nbNk9dwHGM5r1X4a/s0/B34I/tWfDj4e6p8RV8W/EHTtbsE1bQ00Vxp0bTxeayLeSylZDEGU/6v5i\nQMIwIGF8S/8AgpDbeOvC/i+00vwBd6T4j8aWdlBcXz+Irm6srOWweJ42s9Okh8mJT5WSqkMWYkse\nd3Jax+278MNZ+LHh/wDaCm+DK2/xJsL6zvdRv7fXp47W9e1jETbLRoHSEyqACcvtxn5mJap1HoZn\nxg/ZU8IfD7x5pelfFXxy+g+KfGus3dw2haVpLatPpmkSvI1vNMIJ0PnTEKI7dEJ2ncWGCK7i+/4J\n0f2n4o+Hdt4P8V6ha+HvGv8AapvbnxDok2k3ulR6PGJZpHtJZN0iyKT5eCozgFsHIzND/wCCiUv/\nAAmPhL4neOvh5b+JPHXg27vFs9aF/wDZp5tIvI5Y/sN0BbP5rwiU+VcBlZSBlTuk8ztvDH7c9x4t\n+IHwy8N/DbQLPwhB4YvtWle78Z+JrjUbS+h1K1KTQ3t5cxB4d4UhG3FA7INoAJL1DQ8yh/Yi8D/E\nPR/Buv8A7P8A8SpfFdn4w8WN4WX7fo76d9laG0e8nuGLTOzqkSbwoQZBAyGBFc3+0z+xna/A34dW\nvxM8N65q2q6amtvoF5HrWhT6FMLkQtNHPbpcOxmt5AjBXAwCMZJzj62+NXx3+H37NngD4V6d8HtO\n8M6Z4p0TxfP4ouNE8P6zLr1lFA1o9o63N63IkuEk2hVxsQDaMAE/CP7RP7Qnw5+MemeR4O8D6p4Y\nvrnURqNzNe+JrzWIBlJVeCC1mRI41LSBg/LDG1dqkihXB2PkevcPBfjTQ/EWhx/DL4myEaUCf7M1\nPG6bS5m/VoGON6Z46jGAV8PornxmDjWjyy0a1TW6fdf1rs9DGpTUlZnWeNPBeueA9ck0LXYwJABJ\nFLGd0NxC33JYn6MjDoe3IOCCK5OvcPBfjTQ/EWhx/DL4myEaUCf7M1PG6bS5m/VoGON6Z46jGAV8\n78aeC9c8B65JoWuxgSACSKWM7obiFvuSxP0ZGHQ9uQcEEVz4PGS5vYV9Jr7pLuv1XT0s3FOo78st\n/wAzk6KKK9M2P1t/ZG+GniDxH+yRc+KPhf8ACfwr8SvG7fEB9OlHiS0tZhHpP9lwSsEluJYCAsxX\nAVyRvYhTyRq/Eb9if4Z/Ej42/E7WfAd5P4f8FeBU0u31Gx8M6bP4gn/ty7TFzZ2NvAwby4HBMpJx\nExK7QAQnwRoH7Q+seF/gFp/wa8PWMlhqmleNovGlrrcVziSGeGyFpHCsHl9VYCQSeZ/s7O9fU0X/\nAAUE8P6hrnjOXxF8M3TRPiPbabJr9rpetzafO+tafndqNpPDCrQCcbfMgO/JXJkbc4aGmWmiPxL+\nwN4Y+F0/jrxH8ZviLL4f8D+F7nTrPT7+DSJJ73Up9TtluY0NmZVaBokbEisxOQegGa6DxP8AsrW/\nws+GHxj8KaF4i0XxHZRReBLqLV9S0TyL1I9eu3CNa3LXLtZIvBmykhkTKkIRk6nwI+Kug/FmDx94\nIl8OeFm+H2s3Wm3Y8P8Ajbxnc2t7Fd20Rj+2W+oyIJZVAQGZFAKjCoApINX9rL9rD4byeMfip8L/\nAATpNv4r8LeKIPB9h9us75obVE8MyGaWKDEbtLHLvMKyCRdu3eu8EUK4aHVeE/2Efgh4C/aT8GfC\nr4l+MNY1a6vboo1hdeGriz07WSkRlK2uoJd48oDhnGSCMY548n8G/sR+APiRe+OPGnhXxprOpfD/\nAMMahFpsUuleGLq+1Oe/ly0kKWMUrusNupUtMzHcCPlBpYv+Cglh4U0nwL4c+GXgK4sNI8G+IIvE\nAg1nXp9ZfdHbyWps7SWaFTbQeXK2MBsMScctux/C37avw28NW/jTwTafC69tPh94wu4dU/s608S3\nMF/Z6pHkSXEV+kKsUlG0GEptG0cnkE1DQ3fEf7A/gb4aWfjjW/jL8WD4W0jwb4hh0SOaPRJb2S/W\n70+PULZ44o5wySOkoDI3yptYl+AD+aBxnivrT4kftM6P4y+FfiP4T+HvCE+i6brfiaz8RQy3OsTa\npNbi105bBoHkuIvMmMhBl8wuoXOxU2gY+S6pX6kvyCvS9E1vTPE2mQeDvGM4tzbjZpmpvkm0JOfI\nnxy1sxPuYidy5Xcp80orGvQVRWejWz6p/wBffsyJRuaut6Jqfh3U59H1iA291bnDKcEEEZVlYZDK\nwIKsCQwIIODWVXpeia3pnibTIPB3jGcW5txs0zU3yTaEnPkT45a2Yn3MRO5cruU8Treian4d1OfR\n9YgNvdW5wynBBBGVZWGQysCCrAkMCCDg1nQrtv2dTSS+5ruv1XT7m1GXR7mVRRRXWWFFFFAH/9D8\nCLP/AI+4P99f5173pv8AySvxD/2GtG/9JtSrwSz/AOPuD/fX+de96b/ySvxD/wBhrRv/AEm1KssT\n/DX+KP8A6UjnxGy+X5nn9FFFeiAUUUUAFFFFAH2h+x/4C+Ffiuz+LHiv4q+GD4tsvAvhWfWrWxF9\ncWAeeGaNQDLbsGAKsRyGHtmu28YfBL4JfFz4OeGfjn8E4R8MYbnxKPC2uafrmpyXem2M0lubmK8W\n8ePzRDswr7lJyQAvykt5d+yt8Yvhd8LYviRoPxah1mTRvHvh2XQ/M0SG3muYTNKjl8XM0KABVOOW\n5xxivdvDv7Wn7PHw0/4V38Nvh94H1XVvhp4a8RSeJdcOvG1l1PVb5rZ7WJhDEfs6rbqwdEL7WZED\nYwWaHcpWMzwL+wZBq/j34dG9+JHhvxD4C8YasdOl1LSbudWNxbmN5rGIyWxxdSxOTDkbTgltoA3e\nda1+yh4Os/if4p8HD40+DNK0rQ51WG5vb25Mj+dPPEtuVW1BM8AhH2ggBE3oQxDcfUPxJ/b4+F+p\ny/DN/D0ninxTP4F8axeJLi41y1020MtksZRre2WxdVQpkiNXT3L4IVcjwH+1b+yp8PPEPxHu/CMf\njPR28b3cOqR65FYaPNrNvK008l1YRC4eWGO3dXQLJy7EvvGAtGo9D88PjJ8JvFPwN+JeufCvxmYH\n1fQZI0le2dngkWaJJopI2ZUYq8bqwyoPPIBrzGvpT9rz4x+Gfj9+0F4m+LHg+2vbTSdaTT1hj1BI\no7oG1sYLZy6wvIgy8TEYY5BBwDwPmuqRLCiiimIKKKKACpIZpbeVJ4HaOWNgyspIZWByCCOQQehq\nOihoD6a8PeIdI+K+kP4Y8Tof7Xy0oMSgySyEfNdWq8A3BAzPBwLgDcuJh83g/ifwxqXhTUv7P1Db\nIkiiWCeI7obiFvuyxNxlTj2IIKsAwIGDDNLbypPA7RyxsGVlJDKwOQQRyCD0NfS3h7xDpHxX0h/D\nHidD/a+WlBiUGSWQj5rq1XgG4IGZ4OBcAblxMPm+fqU5YKTqU1ek91/L5ry/L025WnTd1t+R8y0V\n0XifwxqXhTUv7P1DbIkiiWCeI7obiFvuyxNxlTj2IIKsAwIHO17tOpGcVKLumdKaaugr6q/Yl+Gn\ngj4v/tO+DPh38R7I6j4c1X+0TdwLLLAXFvp9zcJ+8hZJBiSNT8rDOMHgkV8q19F/sm/F7w38B/j9\n4Y+K3i21u7zStEXUBNFYpG9wxu7C4tU2LK8aHDyqWy4woJGTgGnsUj73+Ef/AAT+8G3X7Uet2Pj2\nU3vwYso7O+0i8E0kSaxD4gby9It4p4yrs25m8xkIO6LnCuDXzzo/7C3iLxrc3evQ+LPDngTRdd8Q\n6lovhe212+lin1SWzunt9kAWOUlVcCLcx3F8DHIJ4/4SftkfEPwvf/Cfwp8QNVutS+Hnwz1u31WK\nwtoojclIXYhQ7tH5nlrI4iV3CqDgEADHuujftVfsz+OND8GWnxv0jxRBe/C7xJq+taG2irZtHfWu\np3/9oC3vBPIpRhIqITGT8oOGycidStDwjw3+xR4/utJ8S698RfE3h/4bab4a1qXw41x4gvHhS51a\nIbngg8mOXcqr8xk+7t+YbgGK/Sn7R/7E9jq37QfxK1Lwvqvhz4WfDjQdR0fR7SfVJpLa1k1K70q1\nuTbW6RRyEsfMMjk4ADbuecctr37XPwI+PfhTxP4R/aP0TxFptvP4su/FGjzeGXtHlRLqPyjZXH2v\navC8+YqklucKBhvdtT/4KP8Awv8AFWufEfTvtnjHwPo/iXWbHW9J1TRYbBtSjeDSrXT7i1u4JpXi\n8pmt96FJCSSCduMUtQ0PlD4vfAGD4O/soXUHi3R7WLx7ofxNutCutQiyzSWcelrOiI5CkwuxEqZU\nEggkDpXwHX3t8WP2lfht8Tfgd4v+G2pXXijVfEE3jBPEui6pqSWbvdRtZQWDx6kYnXYyRI5TyUYE\n7AcDca+CatEyCiiimIKKKKACvSfh58Q5vBs1zpmqWw1bw3qwEeoafIfklTs6H+CVOqsMHIHsR5tR\nWGJw0K0HTqK6f9ff2fQmcFJWZ6n8Q/h5D4chtvFXhW5OreEtWJ+yXePnifqbe4A+5Kn4BgMjuB5Z\nXpPw8+Ic3g2a50zVLYat4b1YCPUNPkPySp2dD/BKnVWGDkD2ItfEP4eQ+HIbbxV4VuTq3hLVifsl\n3j54n6m3uAPuSp+AYDI7gefhsTOlNYfEO9/hl/N5P+9+e66pZQm4vkn8n3/4J5ZRRRXrm4UUUUAf\npj8Jf2TfA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    }
   },
   "cell_type": "markdown",
   "id": "8062bf2b-0270-4ca0-8dac-53970629777d",
   "metadata": {},
   "source": [
    "![AIM321-Package-Register-1.jpg](attachment:da3749da-f1de-4c26-bc2c-700bcc624ff1.jpg)!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "398e4132-8995-4831-bd8d-3c30e49cb65f",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Package & Register Model Steps: Configure step to create and register model\n",
    "\n",
    "Configure metrics captured, package model for inference, & register model version"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd3c6e3-bf5d-4039-a1e2-01039e4ddad4",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 1. Package Model Step\n",
    "\n",
    "Package model step serves the purpose of packaging the model and baseline for model registry. [ModelMetrics](https://sagemaker.readthedocs.io/en/stable/api/inference/model_monitor.html#sagemaker.model_metrics.ModelMetrics) is used to specify paths to .json files within S3 buckets for model evaluation metric from evaluate model step, and for statistic, constraints files from baseline step. These are used for model registry. Model artifacts are specified from train model step. \n",
    "\n",
    "As part of the pipeline, [ModelStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.model_step.ModelStep) configures model package model for inference using model object, model.create().  model.create() allows to specify the stance to run the package model step. This step is required as in Part 2 model monitor will be performed for batch inference job.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "662bfdf2-dd28-4b4e-af67-e130a0770682",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Specify model metric and drift baseline metadata to register\n",
    "model_metrics = ModelMetrics(\n",
    "    model_statistics=MetricsSource(\n",
    "        s3_uri='{}/evaluation.json'.format(\n",
    "            evaluation_step.arguments['ProcessingOutputConfig']['Outputs'][0]['S3Output']['S3Uri']\n",
    "        ),\n",
    "        content_type='application/json',\n",
    "    ),\n",
    "    model_data_statistics=MetricsSource(\n",
    "            s3_uri=baseline_model_data_step.properties.CalculatedBaselineStatistics,\n",
    "            content_type=\"application/json\",\n",
    "    ),\n",
    "    model_data_constraints=MetricsSource(\n",
    "            s3_uri=baseline_model_data_step.properties.CalculatedBaselineConstraints,\n",
    "            content_type=\"application/json\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "drift_check_baselines = DriftCheckBaselines(\n",
    "    model_data_statistics=MetricsSource(\n",
    "        s3_uri=baseline_model_data_step.properties.BaselineUsedForDriftCheckStatistics,\n",
    "        content_type=\"application/json\",\n",
    "    ),\n",
    "    model_data_constraints=MetricsSource(\n",
    "        s3_uri=baseline_model_data_step.properties.BaselineUsedForDriftCheckConstraints,\n",
    "        content_type=\"application/json\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "model = Model(\n",
    "    image_uri=estimator.training_image_uri(),\n",
    "    model_data=training_step.properties.ModelArtifacts.S3ModelArtifacts,\n",
    "    entry_point=estimator.entry_point,\n",
    "    role=role_arn,\n",
    "    sagemaker_session=session\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad26f81a-4249-4e09-8a6a-7a30c5132266",
   "metadata": {},
   "source": [
    "#### 2. Register Model Step\n",
    "\n",
    "Register model step registers model version using metadata, baseline using model object, [model.register()](https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.model.Model.register). model.register() brings the metadata related to model and baseline using ModelMetrics This step uses ModelStep to register model and baseline information to model registry. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "902269de-bc00-4093-bb65-b89e0ffcadab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Configure step to package model for inference using Model object: model.create()\n",
    "\n",
    "step_args = model.create(\n",
    "    instance_type=\"ml.m5.large\",\n",
    "    accelerator_type=\"ml.eia1.medium\",\n",
    ")\n",
    "\n",
    "create_model_step = ModelStep(\n",
    "    name=\"CustomerChurnCreateModel\",\n",
    "    step_args=step_args,\n",
    ")\n",
    "\n",
    "\n",
    "# Configure step to register model version using metadata and Model object: model.register()\n",
    "model_registry_args = model.register(\n",
    "    content_types=['text/csv'],\n",
    "    response_types=['text/csv'],\n",
    "    inference_instances=['ml.t2.medium', 'ml.m5.xlarge'],\n",
    "    transform_instances=['ml.m5.xlarge'],\n",
    "    model_package_group_name=model_package_group_name,\n",
    "    customer_metadata_properties={\"ModelName\": create_model_step.properties.ModelName},\n",
    "    drift_check_baselines=drift_check_baselines,\n",
    "    approval_status='PendingManualApproval',\n",
    "    model_metrics=model_metrics\n",
    ")\n",
    "\n",
    "register_step = ModelStep(\n",
    "    name='RegisterModel',\n",
    "    step_args=model_registry_args\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9de67cb3-35e3-45f8-8fbd-d4fa9e4118d5",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1-C. Conditional Step: Configure step to evaluate model performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b83847-66c2-4597-8171-20e8005bb328",
   "metadata": {},
   "source": [
    "Conditional step is a performance check step which checks the quality of the trained model based on the evaluate model step. It reads the *evaluation.json* file and checks the whether the model accuracy is greater than certain threshold using ConditionGreaterThanOrEqualTo.  Only if the model accuracy is greater than the threshold (If true condition), the pipeline continues to process baseline step, package model step and register model step using [ConditionStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#conditionstep). If the condition is not met for performance check, the pipeline fails."
   ]
  },
  {
   "attachments": {
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E/wD4KC/BjWfC19pHhKPx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\nc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    }
   },
   "cell_type": "markdown",
   "id": "b563ee91-284f-4106-b8ed-baacb3f35bb0",
   "metadata": {},
   "source": [
    "![AIM321-Conditional-Step-1.jpg](attachment:2e517b87-ef0e-45a9-b18f-d649d9f5f935.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14fa758a-59b3-4972-b94f-9862b24864c8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Condition step for evaluating model quality and branching execution\n",
    "\n",
    "cond_gte = ConditionGreaterThanOrEqualTo(\n",
    "    left=JsonGet(\n",
    "        step_name=evaluation_step.name,\n",
    "        property_file=evaluation_report,\n",
    "        json_path='binary_classification_metrics.accuracy.value',\n",
    "    ),\n",
    "    right=0.4,\n",
    ")\n",
    "condition_step = ConditionStep(\n",
    "    name='PerformanceConditionalCheck',\n",
    "    conditions=[cond_gte],\n",
    "    if_steps=[baseline_model_data_step,create_model_step, register_step],\n",
    "    else_steps=[],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6929ef0f-7646-4cbc-91b6-9918bbbcb3d4",
   "metadata": {},
   "source": [
    "At this point, all the seven steps of train and baseline pipeline are defined and configured.\n",
    "\n",
    "1. Prepare Data Step\n",
    "2. Train Model Step\n",
    "3. Evaluate Model Step\n",
    "4. Conditional Step for Performance Check.\n",
    "5. Baseline Step\n",
    "6. Package Model Step\n",
    "7. Register Model Step\n",
    "\n",
    "Baseline step, package model step and register model step are nested within the conditional step, when the performance check condition is satisfied.\n"
   ]
  },
  {
   "attachments": {
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k/KSAQMnHrXF/8FIf20vjX+zp8QdC+H3wwl0+0stY0X7bPPcWv2i4Esk80JCl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\nf+Ckc/wJ+DKfA/xr8Prfxzo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0ES/D/IP7UxH858B/8O8PhR/0Meu/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\nPDWvaqutX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    }
   },
   "cell_type": "markdown",
   "id": "fcd90e14-5bde-4d3e-9a27-32ba056093a9",
   "metadata": {},
   "source": [
    "![AIM321-Pipeline.jpg](attachment:9643e4d2-adeb-4607-9fc5-af12ee877b61.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04bd944-becc-47fc-a426-e5a2cc586ac6",
   "metadata": {},
   "source": [
    "Putting it all together, Pipeline() sets up the train and baseline pipeline with the steps and their configurations. training_pipeline.upsert() creates the pipeline with pipeline role. training_pipeline.start() starts executing the pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14d7462d-b3a0-48c8-8db1-18b3d1c40f90",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pipeline_name = 'train-baseline-pipeline-1'\n",
    "step_list = [\n",
    "             data_preparation_step,\n",
    "             training_step,\n",
    "             evaluation_step,\n",
    "             condition_step]\n",
    "\n",
    "training_pipeline = Pipeline(\n",
    "    name=pipeline_name,\n",
    "    parameters=[\n",
    "        input_data,\n",
    "      ],\n",
    "    steps=step_list\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "544e0b05-5f62-4553-856c-47bc2f558f8c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Viewing the pipeline definition will all the string variables interpolated may help debug pipeline bugs. It is commented out here due to length.\n",
    "#json.loads(training_pipeline.definition())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88783197-ce81-406d-bcb5-329cf0d6c0a1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Note: If an existing pipeline has the same name it will be overwritten.\n",
    "training_pipeline.upsert(role_arn=role_arn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83a590e8-459a-4125-b4ce-6e703c3cfb10",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "execution = training_pipeline.start()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac082fd-fcdf-44b1-83d2-8ffca81cb79b",
   "metadata": {},
   "source": [
    "Once the pipeline starts executing, the status of the pipeline (executing, succeeded or failed) can be found browsing “Pipelines” within SageMaker Studio. The pipeline shows the Directed acyclic graph (DAG) with the 7 steps of the pipeline. For each step, it shows related information like input, output, log, information, overview, settings, details depending on the configuration of the step. The diagram below shows which steps from the pipeline process relate to the steps of the pipeline DAG. Once the train and baseline pipeline executes successfully, it registers the trained model as part of the model group of model registry. Opening “Model registry” within SageMaker Studio, the model group name can be found with its status. Clicking on the appropriate model group name, shows the model version and status. The status of the model has to be updated to “Approved”, as required by the batch inference and model monitor pipeline in Part 2."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "408f0c36-9f4b-4f1d-91e2-0bd7da4dc93f",
   "metadata": {},
   "source": [
    "# View Baseline Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59cdfdce-62d7-4006-a58b-824971a4db3c",
   "metadata": {},
   "source": [
    "This works only after model pipeline execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99ee0d4f-cb57-4958-9cff-7bf7a89396b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.model_monitor import MonitoringExecution\n",
    "from sagemaker.s3 import S3Downloader\n",
    "\n",
    "monitoring_step = [step for step in execution.list_steps() if \"QualityCheck\" in step[\"Metadata\"]][0]\n",
    "s3_baseline_statistics=monitoring_step[\"Metadata\"][\"QualityCheck\"][\"BaselineUsedForDriftCheckStatistics\"]\n",
    "print(\"S3 URI FOR BASELINE STATISTICS ==>\", s3_baseline_statistics)\n",
    "s3_baseline_constraints=monitoring_step[\"Metadata\"][\"QualityCheck\"][\"BaselineUsedForDriftCheckConstraints\"]\n",
    "print(\"S3 URI FOR BASELINE CONSTRAINTS ==>\", s3_baseline_constraints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "497c24bf-c804-40db-88bf-5ca3977cd29d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "statistics = json.loads(S3Downloader.read_file(s3_baseline_statistics))\n",
    "constraints = json.loads(S3Downloader.read_file(s3_baseline_constraints))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4306f82f-a61b-4cb5-92dd-6dbc68cd6d9f",
   "metadata": {},
   "source": [
    "### View Baseline Constraints\n",
    "\n",
    "SageMaker Model Monitor will automatically identify data quality constraints that can be used as-is or customized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5b23b64-eef8-4d3f-ab5d-ab04465c5fc2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "\n",
    "constraints_df = pd.json_normalize(constraints, record_path=['features'])\n",
    "constraints_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e04d671-f2af-4c7a-a385-34a08d7bc86b",
   "metadata": {},
   "source": [
    "### View Baseline Statistics\n",
    "\n",
    "SageMaker Model Monitor will automatically identify statistical calculations for each feature in the baseline dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "974d54fb-40ac-406b-8e39-a1b52dd51a82",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "\n",
    "statistics_df = pd.json_normalize(statistics, record_path=['features'])\n",
    "statistics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98dacb67-bc95-44ed-aa0b-77b8e65705fa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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