Workshop AI/ML: From development to production with SageMaker Script Mode

In this workshop, we will discuss what is Amazon Sagemaker and how it helps in developing and deploying a Machine Learning feature.
In particular, we will focus on how SageMaker integrates with the most-known frameworks for Machine Learning and Deep Learning, including SKLearn, MXNet, and TensorFlow.
We will discuss the best practices for SageMaker, and how to move from a POC to a production-ready platform.

Level: Intermediate/Expert
Language: French / English
Target Audience: Data Scientist/Data Engineer

Transcript

1. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker Develop, Train, Tune and Deploy Machine Learning Models
2. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 1. ML on AWS 2. Amazon SageMaker 3. Demo: Scikit-Learn on SageMaker, from research to API
3. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Put machine learning in the hands of every developer and data scientist ML @ AWS: Our mission
4. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Customer Running ML on AWS Today
5. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS ML Stack P L A T F O R M S A P P L I C A T I O N S E R V I C E S F R A M E W O R K S E T I N F R A S T R U C T U R E Amazon SageMaker Vision Rekognition Image Rekognition Video Amazon Textract Speech Amazon Polly Amazon Transcribe Language Amazon Lex Amazon Translate Amazon Comprehend Forecasting Amazon Forecast Personalization Amazon Personalize DL AMI EC2 P3, C5, F1 Greengrass Elastic Inference
6. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Build, Train and Deploy your Machine Learning Models Amazon SageMaker Fast & accurate data labeling Built-in, high performance algorithms & notebooks BUILD 1 One-click training and tuning TRAI N Model tuning and optimization 2 Fully managed hosting with auto-scaling and elastic inference One-click Deployment of model or Inference Pipeline DEPLOY 3
7. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker: TensorFlow workflow example Develop on Notebook instance with TensorFlow Jupyter kernel BUILD 1 Train custom code with TensorFlow Estimator TRAI N Model tuning and optimization with SageMaker HPO 2 Deploy with a simple .deploy() call DEPLOY 3 Stream training data with PipeModeDataset Distributed training with Horovod Monitor with TensorBoard
8. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker: algorithms A. Train and deploy a built-in algorithm B. Train and deploy your own code in a framework container C. Train and deploy your own container D. Deploy a pre-trained model E. NEW – Train and Deploy a Marketplace Algorithm F. NEW – Deploy a Marketplace Model Package
9. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker Built-in algorithms Linear Learner Factorization Machines XGBoost Image Classification Object Detection Sequence to Sequence DeepAR Forecasting K-Nearest Neighbors BlazingText (classifier) Object2Vec Semantic Segmentation K-Means Principal Component Analysis Latent Dirichlet Allocation Neural Topic Model Random Cut Forest BlazingText (word2vec) IP Insights
10. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker: interface A. Console B. AWS Command Line Interface (CLI): aws sagemaker create-endpoint --endpoint-name <value> --endpoint-config-name <value> C. SageMaker Python SDK model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge') D. Boto3 (python) SageMaker client client.create_endpoint(EndpointName='string', EndpointConfigName='string') E. Spark SDK
11. Python SDK: Amazon Algorithm
12. Python SDK: Deep Learning Estimator
13. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker Deployment & Inference A. Deploy model to Amazon SageMaker HTTPS endpoint B. Run Amazon SageMaker batch-transform job (for batch processing such as evaluating on the test split of the dataset) C. On-device deployment (AWS Greengrass)
14. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. In summary:
15. Demo time !
16. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Pipeline design
17. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Raw Data Algorithm ML Model ETL CI/CD Pipeline Prepared Data Packaged Code ML Pipeline Cleansed data / features Training and testing Compiled / containerized
18. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. SageMaker with Step Functions https://tinyurl.com/y3qthfhe
19. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. SageMaker with Airflow https://tinyurl.com/y4f6f3th
20. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Demonstration
21. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. https://aws.amazon.com/sagemaker http://d2l.ai/ Getting Started