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I have 25+ years of experience architecting and building enterprise applications. I am recognized as an AWS Machine Learning Hero and have earned the AWS Machine Learning Specialty certification. I recently made the transition from Java to ML, so I understand the journey! I have put this guide together for developers looking to upskill on ML. I have hand-selected my favorite sessions for developers who are new to ML, wanting to train sophisticated models using Amazon SageMaker, looking to automate and operationalize ML pipelines, or simply looking to add intelligence to existing applications.  \nPrincipal Training Architect  \nA Cloud Guru  \n© 2021, Amazon Web Services, Inc. or its affiliates rights reserved  \nBreakout sessions  \n© 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved.  \nBREAKOUT SESSIONS  \nINTERACTIVE SESSIONS  \nOTHER RECOMMENDATIONS  \nKEYNOTES  \nLEADERSHIP SESSIONS  \nLEARN PLAY  \nRESERVED SEATING  \nMAKE A PLAN  \nUSEFUL RESOURCES  \nI have chosen these sessions to help you upskill on building, training, and deploying sophisticated modelson AWS.  \nAIM407  \nTrain deep learning ML models at scale with Amazon SageMaker  \nI love this session because it uses real-world examples of computer vision and natural language processing to show you how to train large models in the shortest amount of time. I’m all about saving money on training costs so I’m definitely not going to miss this one!  \nAIM301  \nUse Amazon SageMaker to develop high-quality ML models faster  \nI’m looking forward to this session because it dives deep on SageMaker features that help boost your productivity at every stage of the machine learning lifecycle.  \n© 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved.  \nAIM319  \nPrepare data for ML with ease, speed, and accuracy  \nI’m looking forward to this session because it showcases 3 powerhouse SageMaker features:  \nSageMaker Ground Truth, SageMaker Data Wrangler, and SageMaker Feature Store. This session is all about data.  \nAIM321  \nUse AutoML to create high-quality models  \nThis session is exciting because it shows you how to train hundreds of models at once, allowing you to choose the model that performs the best. SageMaker Autopilot gives you a jumpstart if you don’t have a deep background in ML.  \nAIM320  \nImplementing MLOps practices with Amazon SageMaker  \nMLOps is sometimes an afterthought so I’m excited about this session! This session brings MLOps to the forefront and shows you how to automate and manage your machine learning pipelines.  \n© 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved.  \nBREAKOUT SESSIONS  \nINTERACTIVE SESSIONS  \nOTHER RECOMMENDATIONS  \nKEYNOTES  \nLEADERSHIP SESSIONS  \nLEARN PLAY  \nRESERVED SEATING  \nMAKE A PLAN  \nUSEFUL RESOURCES  \nAIM408  \nAchieve high performance and cost-effective model deployment  \nI’m all about saving money when deploying models and making them accessible in production. I’m interested in this session because it breaks down the different inference options and cost-effective techniques available via Amazon SageMaker.  \nAIM203  \nTurn your documents into insights with AWS machine learning services  \nAIM206  \nHow Pinterest improved WFH employee productivity with intelligent search  \nI love hearing how customers are using AWS machine learning services in the real world. This session piques my interest because AWS customers will share their journeys to find critical data hidden inside documents using AWS machine learning services!  \nI’m the first person to complain about search engines that don’t help me find what I’m searching for. I’m a huge Pinterest fan so I can’t wait to hear how they built a robust search engine using Amazon Kendra to help their employees boost productivity while working from home during the pandemic.  \n© 2021, Amazon Web Services, Inc. or its affiliates","cbCaiqWPYrG8kVXM","https://ap.wps.com/l/cbCaiqWPYrG8kVXM","pdf",21507369,47,"English","# Breakout sessions\n## Keynotes\n## Leadership sessions\n# Interactive sessions\n## Learn play\n# Reserved seating\n# Make a plan\n# Useful resources","[{\"question\":\"Which AWS services are emphasized for training and building ML models?\",\"answer\":\"The guide focuses on Amazon SageMaker for training and development, including SageMaker JumpStart and related capabilities to accelerate model creation.\"},{\"question\":\"How does the guide recommend preparing data for machine learning?\",\"answer\":\"It highlights data-focused SageMaker features such as Ground Truth, Data Wrangler, and Feature Store to improve ease, speed, and accuracy.\"},{\"question\":\"What topics are covered for deploying and operating ML systems?\",\"answer\":\"Sessions address implementing MLOps with SageMaker, managing end-to-end ML workflows, detecting model drift in production, and using inference options and cost-effective techniques for deployment.\"}]","Machine Learning Attendee Guide 2021 | PDF",1789933956,118]