In this episode of AWS TechChat, we close out our four parts of AWS re:Invent 2020 series with an AI/ML special. We cover Amazon Sagemaker, Amazon Kendra, Amazon Elastic MapReduce (EMR), Amazon QuickSight, and some brand new services. We talk about AWS HealthLake and how it makes sense of health data. AWS customers can use Kendra’s Google Drive connector to ingest and manage content from Google Docs and Google Slides. We introduce AWS Panorama which will help improve your operations with computer vision at the edge. We continue with a raft of new Amazon SageMaker updates: • Amazon SageMaker Feature Store - A fully managed repository for machine learning features • Amazon SageMaker Clarify - Bias Detection and Explainability • Amazon SageMaker Debugger - Optimize ML models with real-time monitoring of training metrics and system resources • Amazon SageMaker Model Monitor - Detect drift in model quality, model bias, and feature importance • Amazon SageMaker Pipelines - First purpose-built CI/CD service for machine learning • Amazon SageMaker Jumpstart - Simplifies Access to Pre-built Models and Machine Learning Solutions Before wrapping out, we share two more AI/ML updates - Amazon EMR Studio is the integrated development environment (IDE) for applications written in R, Python, Scala, PySpark, and Jupyter notebooks now gives you the option to deploy on Amazon Elastic Kubernetes Service (EKS). Amazon QuickSight allows you to ask Natural Language Query (NLQ) about your data and get answers in seconds. Speakers: Shane Baldacchino - Edge Specialist Solutions Architect, ANZ, AWS Shai Perednik - Solutions Architect, AWS Pallavi Nargund - Solutions Architect, AWS