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Amazon reInvent 2020 Recap: AI and Machine Learning

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Amazon reInvent 2020 Recap: AI and Machine Learning

Video here: https://youtu.be/YSXe02Y5pHM

NEW RELEASE! Build, Automate, Manage, and Scale ML Workflows with the NEW Amazon SageMaker Pipelines by Hallie Crosby Weishahn.

Description of Talk and Demo

AWS recently announced Amazon SageMaker Pipelines (https://aws.amazon.com/sagemaker/pipelines/), the first purpose-built, easy-to-use Continuous Integration and Continuous Delivery (CI/CD) service for machine learning.

SageMaker Pipelines has three main components which improve the operational resilience and reproducibility of your workflows: 1) pipelines, 2) model registry, and 3) projects.

In this talk and demo, Hallie will walk us through the new Amazon SageMaker Pipelines feature including MLOps support.

Date/Time

9-10am US Pacific Time (Third Monday of Every Month)

RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154

Meetup:

https://www.meetup.com/Data-Science-on-AWS/

Zoom:

https://zoom.us/j/690414331
Webinar ID: 690 414 331

Phone:

+1 646 558 8656 (US Toll) or +1 408 638 0968 (US Toll)

Related Links

Meetup: https://meetup.datascienceonaws.com

GitHub Repo: https://github.com/data-science-on-aws/

O'Reilly Book: https://datascienceonaws.com

YouTube: https://youtube.datascienceonaws.com

Slideshare: https://slideshare.datascienceonaws.com

Support: https://support.pipeline.ai

Monthly Workshop: https://www.eventbrite.com/e/full-day-workshop-kubeflow-gpu-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-tickets-63362929227

RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154

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Amazon reInvent 2020 Recap: AI and Machine Learning

  1. 1. © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. https://www.amazon.com /dp/1492079391/
  2. 2. Data Science on AWS Meetup Dec 21, 2020
  3. 3. © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD CONTACT CENTERS Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Trainium Inferentia FPGA AI SERVICES ML SERVICES FRAMEWORKS & INFRASTRUCTURE DeepGraphLibrary Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Lex Amazon Personalize Amazon Forecast Amazon Comprehend +Medical Amazon Textract Amazon Kendra Amazon CodeGuru Amazon Fraud Detector Amazon Translate INDUSTRIAL AI CODE AND DEVOPS NEW Amazon DevOps Guru Voice ID For Amazon Connect Contact Lens NEW Amazon Monitron NEW AWS Panorama + Appliance NEW Amazon Lookout for Vision NEW Amazon Lookout for Equipment The AWS ML Stack NEW Amazon HealthLake HEALTH AI NEW Amazon Lookout for Metrics ANOMALY DETECTION Amazon Transcribe Medical Amazon Comprehend Medical Amazon SageMaker Label data NEW Aggregate & prepare data NEW Store & share features Auto ML Spark/R NEW Detect bias Visualize in notebooks Pick algorithm Train models Tune parameters NEW Debug & profile Deploy in production Manage & monitor NEW CI/CD Human review NEW: Model management for edge devices NEW: SageMaker JumpStart SAGEMAKER STUDIO IDE
  4. 4. © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD CONTACT CENTERS AI SERVICES Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Lex Amazon Personalize Amazon Forecast Amazon Comprehend +Medical Amazon Textract Amazon Kendra Amazon CodeGuru Amazon Fraud Detector Amazon Translate INDUSTRIAL AI CODE AND DEVOPS NEW Amazon DevOps Guru Voice ID For Amazon Connect Contact Lens NEW Amazon Monitron NEW AWS Panorama + Appliance NEW Amazon Lookout for Vision NEW Amazon Lookout for Equipment AI Services: Easily add intelligence to application NEW Amazon HealthLake HEALTH AI NEW Amazon Lookout for Metrics ANOMALY DETECTION Amazon Transcribe Medical Amazon Comprehend Medical
  5. 5. © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker Label data Aggregate & prepare data Store & share features Auto ML Spark/R Detect bias Visualize in notebooks Pick algorithm Train models Tune parameters Debug & profile Deploy in production Manage & monitor CI/CD Human review Ground Truth NEW Data Wrangler NEW Feature store Autopilot Processing NEW Clarify Studio Notebooks Built-in or Bring-your-own NEW Experiments Spot Training Distributed Training Automatic Model Tuning Debugger NEW Model Hosting Multi-model Endpoints Model Monitor NEW Pipelines Augmented AI NEW: AMAZON SAGEMAKER EDGE MANAGER SAGEMAKER STUDIO IDE AMAZON SAGEMAKER JUMPSTART ML SERVICES ML Services: Amazon SageMaker
  6. 6. © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker overview
  7. 7. 7© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | SageMaker JumpStart Easily and quickly bring machine learning applications to market Leverage solutions out-of-the-box or customize for a specific business problem 15+ pre-built solutions for common ML use cases Use one-click deployable ML models and algorithms from popular model zoos Accelerate time to deploy over 150 open source models Easily bring ML applications to market using pre-built solutions, ML models and algorithms from popular model zoos, and getting started content Get started with just a few clicks
  8. 8. 8© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker JumpStart pre-built solutions Predictive Maintenance Predictive maintenancefor manufacturing > Predictive maintenance for vehicle fleets > Demand Forecasting Demand forecasting with deep learning > Fraud Detection Detect malicious users and transactions > Fraud detection in financial transactions using deep graph library > Credit Risk Prediction Explain credit decisions > Extract & Analyze Data from Documents Document summarization, entity, and relationship extraction > Handwriting recognition > Filling in missing values in tabular records > Differential privacy for sentiment classification > Computer Vision Product defect detection in images > Autonomous Driving Visual perception with active learning > Personalized Recommendations Entity resolution in identity graphs > Purchase modeling > Churn Prediction Churn prediction with text > Learn more about solutions: https://aws.amazon.com/sagemaker/getting-started/
  9. 9. 9© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker JumpStart open source models 150+ pre-trained open source models from PyTorch Hub & TensorFlow Hub TASKS MODELS VISION TEXT
  10. 10. 10© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Easily launch solutions and deploy or fine-tune models Launch solutions, or deploy or fine-tune pre-trained models with a single click Easily manage assets from Amazon SageMaker JumpStart Open pre-populated notebooks for solutions and inference on deployed models
  11. 11. 11© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | SageMaker Data Wrangler The fastest and easiest way to prepare data for machine learning Support for data from multiple sources Quickly select and query data Use built-in data transformations to covert raw data to features for machine learning Easily transform data with built-in data transformations Complete flexibility to bring your own custom transformations in in PySpark, SQL, or Pandas Customize data transformations Quickly detect outliers or extreme values – all without writing code Understand data visually Diagnose potential issues in data preparation workflows that could hinder ML model accuracy Quickly estimate ML model accuracy Deploy data preparation workflows into production with a single click Manage all steps of the data preparation workflow through a single visual interface to quickly operationalize workflows into production settings
  12. 12. 12© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | SageMaker Data Wrangler Use Cases Cleanse & Explore Data Use built-in data transformations to accelerate data cleansing and exploration Visualize & Understand Data Enrich Data Quickly detect outliers or extreme values within a data set without the need to write code Use built-in data transformation tools to transform data into formats that can be used to build accurate ML models
  13. 13. 13© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Quickly select and query data Select data from Amazon Athena, Amazon Redshift, AWS Lake Formation, Amazon S3, and features from SageMaker Feature Store Write queries for data sources before importing data over to SageMaker Data Wrangler Import data in various file formats, such as CSV files, Parquet files, and database tables directly into Amazon SageMaker
  14. 14. 14© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Easily transform data Transform your data without writing a single line of code using over 300 built-in data transformations Built-in data transformations include convert column type, rename column, and delete column Author custom transformations in PySpark, SQL, and Pandas
  15. 15. 15© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Understand your data visually Intuitively understand your data with a set of pre-configured visualization templates Pre-configured visualization templates include histograms, scatter plots, box and whisker plots, line plots, and bar charts Interactively create and edit your own visualizations so you can quickly detect outliers or extreme values
  16. 16. 16© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Quickly estimate model accuracy Identify inconsistencies in data preparation workflows and diagnose issues before ML models are deployed into production Select subsets of data to identify errors Identify which features are contributing to model performance relative to others Determine if additional feature engineering is needed to improve model performance
  17. 17. 17© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Deploy data preparation workflows into production Export data preparation workflows to a notebook or Python code Integrate your workflow with SageMaker Pipelines to automate model deployment and management Publish created features to SageMaker Feature Store for reuse and syndication across teams and projects
  18. 18. 18© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker Debugger Detect bottlenecks and training problems in real-time, and train models faster Detect bottlenecks and issues during training in real-time and correct problems to deploy models faster, with a single, unified tool Generate ML models faster Monitor and profile system resources without code, and get recommendations to optimize resources effectively Optimize resources with no additional code Get complete insights into the ML training process in real-time and offline Make ML training transparent
  19. 19. 19© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Automatic detection of errors with visualization of alerts 1 2 Alerts to resolve errors during ML training runs Automatic detection of common training errors such as gradient values becoming too large or too small 3 Visualization of alerts with Amazon SageMaker Studio or with the SageMaker Debugger SDK
  20. 20. 20© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Monitor and profile system resource utilization Automatically monitor system resource utilization Profile training jobs to collect ML framework metrics Visualize system resource utilization for GPU, CPU, network, memory within SageMaker Studio
  21. 21. 21© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Analyze errors and take action Built-in analysis in the form of rules Automatically analyze training data including inputs, outputs, tensors Detect if a model is overfitting or overtraining, or determine if gradient values are not correct Specify custom actions to stop training or send alerts
  22. 22. © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Trainium Inferentia FPGA FRAMEWORKS & INFRASTRUCTURE DeepGraphLibrary Frameworks & Infrastructure
  23. 23. © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. DRAFT Habana-based instances C O M I N G 2 0 2 1 EC2 instances powered by accelerator chips named Habana Gaudi from Habana Labs, an Intel company Compute Coming 2021
  24. 24. © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. Most TFLOPS compute power vs. any other machine learning instance in the cloud Use the same Neuron SDK as Inferentia instances DRAFT AWS Trainium C O M I N G 2 0 2 1 First ML chip for training—will be the most cost effective in the cloud Compute Coming 2021
  25. 25. Thank you! © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. Chris Fregly Twitter @cfregly Antje Barth Twitter @anbarth https://www.amazon.com /dp/1492079391/

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