rights reserved. Platform Services AWS ML Stack Deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference.
rights reserved. Data Visualization & Analysis Business Problem – M L problem fram ing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering M odel Training & Param eter Tuning M odel Evaluation Are Business Goals met? M odel Deploym ent M onitoring & Debugging Yes No Data Augmentation Feature Augmentation The Machine Learning Process Re-training Predictions
rights reserved. Data Visualization & Analysis Business Problem – M L problem fram ing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering M odel Training & Param eter Tuning M odel Evaluation Are Business Goals met? M odel Deploym ent M onitoring & Debugging Yes No Data Augmentation Feature Augmentation Problem discovery Re-training • Help formulate the right questions • Dom ain Know ledge Predictions
rights reserved. Data Visualization & Analysis Business Problem – M L problem fram ing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering M odel Training & Param eter Tuning M odel Evaluation Are Business Goals met? M odel Deploym ent M onitoring & Debugging Yes No Data Augmentation Feature Augmentation Retraining • Need a data platform? • Am azon S3 • AW S Glue • Am azon Athena • Am azon EM R • Am azon Redshift Spectrum Integration Predictions
rights reserved. Data Visualization & Analysis Business Problem – M L problem fram ing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering M odel Training & Param eter Tuning M odel Evaluation Are Business Goals met? M odel Deploym ent M onitoring & Debugging Yes No Data Augmentation Feature Augmentation Retraining Model Training Predictions • Setup and manage Notebook Environments • Setup and manage Training Clusters • Write Data Connectors • Scale ML algorithms to large datasets • Distribute ML training algorithm to multiple machines • Secure Model artifacts
rights reserved. Data Visualization & Analysis Business Problem – M L problem fram ing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering M odel Training & Param eter Tuning M odel Evaluation Are Business Goals met? M odel Deploym ent M onitoring & Debugging Yes No Data Augmentation Feature Augmentation Retraining Model Deployment Predictions • Setup and manage Model Inference Clusters • Manage and Scale Model Inference APIs • Monitor and Debug Model Predictions • Models versioning and performance tracking • Automate New Model version promotion to production (A/B testing)
rights reserved. End-to-End Machine Learning Platform Zero setup Flexible Model Training Pay by the second $ Amazon SageMaker Build, train, and deploy machine learning models at scale
rights reserved. Highly-optimized machine learning algorithms One-click training for ML, DL, and custom algorithms Build Pre-built notebook instances Easier training with hyperparameter optimization Train Amazon SageMaker
rights reserved. One-click training for ML, DL, and custom algorithms Easier training with hyperparameter optimization Highly-optimized machine learning algorithms Deployment without engineering effort Fully-managed hosting at scale Build Pre-built notebook instances Deploy Train Amazon SageMaker
rights reserved. Amazon ECR Model Training (on EC2) Model Hosting (on EC2) Training data Model artifacts T r a in in g c o d e H e lp e r c o d e H e lp e r c o d e In f e r e n c e c o d e Ground Truth C lie n t a p p lic a t io n In f e r e n c e c o d e T r a in in g c o d e In f e r e n c e r e q u e s t In f e r e n c e r e s p o n s e In f e r e n c e E n d p o in t Amazon SageMaker
rights reserved. Bring your own container https://github.com/aws/sagemaker-container-support • Integration with SageMaker Python SDK Estimators, including: • Downloading user-provided Python code • Deserializing hyperparameters (preserving their Python types) • bin/entry.py, the Docker entrypoint required by SageMaker • Reading in the metadata files provided to the container during training • nginx + Gunicorn HTTP server for serving inference requests https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/scikit_bring_your_own https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/r_bring_your_own
rights reserved. End-to-End Machine Learning Platform Zero setup Flexible Model Training Pay by the second $ Amazon SageMaker Build, train, and deploy machine learning models at scale
rights reserved. Example #1: binary classifier with Linear Learner built-in algo https://github.com/awslabs/amazon-sagemaker- examples/tree/master/introduction_to_amazon_algorithms/linear_learner_mnist Example #2: multi-class classifier with XGBoost built-in algo (low-level API) https://github.com/awslabs/amazon- sagemaker-examples/tree/master/introduction_to_amazon_algorithms/xgboost_mnist Example #3: bring your own code (TensorFlow or MXNet, we should let the participants choose) • https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python- sdk/tensorflow_distributed_mnist • https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python- sdk/mxnet_gluon_sentiment Example #4: bring your own model (TensorFlow) and deploy it on SageMaker: https://github.com/awslabs/amazon- sagemaker-examples/tree/master/advanced_functionality/tensorflow_iris_byom Example #5 (optional): bring your own container https://github.com/awslabs/amazon-sagemaker- examples/blob/master/advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb
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