Your guide to AI and ML at AWS re:Invent 2021

Its practically here! Only 9 days until AWS re: Invent 2021, and were very excited to share some highlights you may enjoy this year. The AI/ML group has been striving to provide some incredible material and this year, we have more session types for you to enjoy. Back face to face, we now have chalk talks, workshops, home builders sessions, and our conventional breakout sessions. In 2015 we hosted the first-ever artificial intelligence (ML) keynote, and we are continuing the custom. We likewise have more interactive and enjoyable events occurring with our AWS DeepRacer League and AWS BugBust Challenge. There are over 200 AI/ML sessions, including breakout sessions with clients such as Aon Corporation, Qualtrics, Shutterstock, and Bloomberg.
To help you plan your agenda for this years re: Invent, here are some highlights of the AI/ML track. You can likewise get the scoop from some of our AI/ML Community Heroes. So buckle up, and start signing up for your preferred sessions.
Swami Sivasubramanian keynote
Join Swami Sivasubramanian, Vice President, Amazon Machine Learning, on an exploration of what it takes to put data in action with an end-to-end information method consisting of the current news on databases, analytics, and ML.
Speed up development with machine learning leadership session
With the increase in compute power and information proliferation, ML has actually moved from the peripheral to being a core part of organizations and organizations throughout markets. AWS consumers use ML and AI services to make precise forecasts, get deeper insights from their information, minimize functional overhead, improve customer experiences, and develop entirely brand-new lines of organization. In this session, speak with Bratin Saha, Vice President, Amazon Machine Learning, and explore how AWS services can assist you move from idea to production with ML.
AI/ML session preview
Heres a sneak peek of some of the different sessions were using this year by session type. You can constantly log in to the event website to favorite or register for any of these sessions, or browse the brochure for over 200 other sessions offered.
Breakout sessions
Prepare information for ML with speed, precision, and ease (AIM319).
Join this session to learn how to prepare data for ML in minutes utilizing Amazon SageMaker. Stroll through a complete data-preparation workflow, consisting of how to label training datasets using SageMaker Ground Truth, as well as how to extract data from several information sources, transform it utilizing the prebuilt visualization templates in SageMaker Data Wrangler, and create design functions.
Achieve high performance and affordable model deployment (AIM408).
To optimize your ML investments, high performance and cost-effective strategies are needed to scale design releases. In this session, find out about the deployment options readily available in Amazon SageMaker, consisting of enhanced infrastructure choices; real-time, asynchronous, and batch reasonings; multi-container endpoints; multi-model endpoints; automobile scaling; model tracking; and CI/CD combination for your ML workloads. Discover how to pick a much better inference choice for your ML usage case. Then, speak with Goldman Sachs about how they use SageMaker for quickly, low-latency, and scalable releases to offer pertinent research content suggestions for their clients.
Implementing MLOps practices with Amazon SageMaker, featuring Vanguard (AIM320).
Carrying out MLOps practices assists data researchers and operations engineers work together to prepare, develop, train, release, and manage models at scale. Throughout this session, check out the breadth of MLOps functions in Amazon SageMaker that help you arrangement constant model advancement environments, automate ML workflows, carry out CI/CD pipelines for ML, monitor designs in production, and standardize design governance abilities. Then, hear from Vanguard as they share their journey making it possible for MLOps to accomplish ML at scale for their polyglot model development platforms using SageMaker functions, consisting of SageMaker jobs, SageMaker Pipelines, SageMaker Model Registry, and SageMaker Model Monitor.
Enhancing the customer experience with Amazon Personalize (AIM204).
Customizing material for a consumer online is essential to breaking through the sound. Yet, brand names deal with difficulties that typically prevent them from offering these seamless, pertinent experiences. Find out how simple it is to use Amazon Personalize to customize product and content suggestions to ensure that your users are getting the content they want, causing increased engagement and retention.
AI/ML for sustainability development: Insight at the edge (AIM207).
As environment change, wildlife conservation, public health, economic and racial equity, and new energy solutions become increasingly synergistic, scalable services are required for actionable analysis at the crossway of these fields. In this session, discover how the power of AI/ML and IoT can be brought as close as possible to the tough edge environments that offer information to develop these insights. Also discover how AWS puts AI/ML in the hands of the largest-scale fisheries on earth, and how companies can take advantage of data to support more sustainable, durable supply chains.
Get started with AWS computer system vision services (AIM204).
This session provides an overview of AWS computer system vision services and demonstrates how these pretrained and adjustable ML capabilities can assist you begin rapidly– no ML expertise required. Discover how to deploy these designs onto the gadget of your choice to run a reasoning in your area or utilize cloud APIs for your specific computing needs. Discover first-hand how Shutterstock utilizes AWS computer vision services to produce efficiency at scale for media analysis, material small amounts, and quality evaluation use cases.
Chalk talk sessions.
Develop an ML-powered demand preparation system using Amazon Forecast (AIM310).
This chalk talk checks out how you can use Amazon Forecast to develop an ML-powered, totally automated need preparing system for your business or your multi-tenant SaaS platform without requiring any ML competence. Forecast automatically produces extremely precise forecasts utilizing ML, explains the drivers behind those projections, and keeps your ML models constantly as much as date to capture brand-new trends.
Hi, is it conversational AI youre searching for? (AIM305).
Clients employing for assistance anticipate a customized experience and a quick resolution to their problem. With chatbots, you can supply automatic and human-like conversational experiences for your customers. In this chalk talk, talk about techniques to develop personalized experiences using Amazon Lex and Amazon Polly. Check out how to create discussion paths, customize responses, integrate with your applications, and allow self-service usage cases to scale your consumer assistance functions.
Harness the power of ML to protect your service with Amazon Fraud Detector (AIM308).
In this session, find out how Amazon Fraud Detector transforms raw information into extremely accurate ML-based scams detection designs. Discover how the service does data preparation and recognition, feature engineering, information enrichment, and model training and tuning.
Deep knowing applications with PyTorch (AIM404).
By utilizing PyTorch in Amazon SageMaker, you have a versatile deep knowing framework combined with a completely handled ML option that permits you to shift perfectly from research prototyping to production release. In this session, speak with the PyTorch group on the most recent features and library releases. Also, find out how to develop with PyTorch utilizing SageMaker for crucial use cases, such as using a BERT model for natural language processing (NLP) and instance division for fine-grained computer vision with distributed training and design parallelism.
Explore, examine, and procedure information using Jupyter notebooks (AIM324).
Before using a dataset to train a design, you require to check out, examine, and preprocess it. Throughout this chalk talk, discover how to utilize Amazon SageMaker to finish these jobs in a Jupyter notebook environment.
Artificial intelligence at the edge with Amazon SageMaker (AIM410).
More ML designs are being released on edge gadgets such as robots and wise cams. In this chalk talk, dive into constructing computer vision (CV) applications at the edge for predictive maintenance, industrial IoT, and more. Learn how to run and keep an eye on multiple models across a fleet of devices. Likewise walk through the process to construct and train CV models with Amazon SageMaker and how to package, deploy, and manage them with SageMaker Edge Manager. The chalk talk also covers edge device setup and MLOps lifecycle with over-the-air design updates and data capture to the cloud.
Contractors sessions.
Build and release a custom computer system vision design in 60 minutes (AIM314).
Amazon Rekognition Custom Labels is an automatic ML function that makes it possible for consumers to quickly train their own custom designs for finding business-specific items and scenes from images– no ML expertise is needed. In this builders session, learn how to utilize Amazon Rekognition Custom Labels to build and deploy your own computer vision model and push it to an application to showcase reasoning on images from a camera feed. Bring your laptop and an AWS account.
Easily label training information for machine knowing at scale (AIM406).
Join this session to discover how to produce top quality labels while likewise lowering your information labeling costs by approximately 70%. This builders session walks through the various workflow alternatives in Amazon SageMaker Ground Truth, such as automatic labeling and assistive labeling features like auto-segmentation and image label confirmation. It also information how to build extremely precise training datasets for company brand name logo designs, so you can build an ML design for company brand security.
Workshop sessions.
Establish your ML task with Amazon SageMaker (AIM402).
In this workshop, learn how to establish a complete ML job end to end with Amazon SageMaker. Start with data exploration and analysis, data cleaning, and function engineering with SageMaker Data Wrangler. Store functions in SageMaker Feature Store, extract features for training with SageMaker Processing, train a design with SageMaker training, and then release it with SageMaker hosting. Learn how to use SageMaker Studio as an IDE and SageMaker Pipelines for managing the ML workflow.
End-to-end 3D maker finding out on Amazon SageMaker (AIM414).
The growing schedule of lidar sensors has actually increased use of point cloud data for ML tasks like 3D object detection, segmentation, item synthesis, and reconstruction. The design in this session will be trained on a self-governing car dataset.
AI workflow automation for file processing (AIM316).
Mortgage packets have numerous files in different layouts and formats. With ML, you can establish a document-processing pipeline to automate home loan application workflows like extracting text from Deeds, paystubs, and w2s; classifying files; or using custom entity recognition to take out specific information points. In this workshop, learn different methods to use optical character acknowledgment (OCR), NLP, and human-in-the-loop services to build a document-processing pipeline to automate home mortgage applications– conserving time, minimizing manual effort, and improving ROI for your organization.
Boost the worth of your media content with ML-powered search (AIM315).
Applying synthetic intelligence and ML abilities like image and video analysis, audio transcription, maker translation, and text analytics can resolve many of these issues. In this workshop, use ML to extract in-depth metadata from material and make it offered for discovery, modifying, and search utilize cases.
Immediately find and diagnose abnormalities within your company data (AIM302).
Anomalies in company information typically suggest possible issues or perhaps chances. ML can help you detect abnormalities and then act upon them proactively. In this workshop, discover how Amazon Lookout for Metrics immediately identifies anomalies across thousands of metrics in near-real time and reduces incorrect alarms.
Join the very first yearly AWS BugBust re: Invent Challenge and aid set a Guinness record.
Python and Java designers of all ability levels can compete to repair software application bugs, earn points, and win a selection of rewards consisting of Amazon Echo Dots, hoodies, and the grand reward of $1,500 USD. All signed up individuals who repair even one bug will receive special prizes and a certificate from AWS and Guinness to celebrate their contribution. You can sign up with the obstacle practically or in-person at the AWS BugBust Hub in the main expo.
AWS DeepRacer: The fastest way to get rolling with maker learning.
Designers of all ability levels from novices to experts can get hands-on with ML by using AWS DeepRacer to train designs in a cloud-based 3D racing simulator. Racers from practically throughout the world can contend in the AWS DeepRacer League, the very first international self-governing racing league driven by support learning. The race is on now! Check in to AWS DeepRacer and complete in the AWS re: Invent Open for prizes and glory now through December 31, 2021. Tune in to the AWS DeepRacer League Championships on Twitch November 19 and 22 to see the 40 fastest designers of the 2021 season contend live. Gain from the best as they contend for a chance to advance to the Championship Cup Finale throughout Swami Sivasubramanians keynote on December 1, where they will race for their shot at $20,000 USD in cash rewards and the right to hoist the Championship Cup!
For those attending re: Invent in Las Vegas, dont lose out on the opportunity to take your design from Sim2Real (simulation to reality) on the AWS DeepRacer Speedway inside the material center at Caesars Forum. Upload your design and race a 1/18th scale autonomous RC cars and truck on a physical track. Come by Tuesday afternoon to participate in the livestreamed wildcard race for an opportunity to win a journey back for re: Invent 2022. No model? No issue! The brand new AWS DeepRacer Arcade is readily available in the exposition, where you can get literally get in the drivers seat and take the wheel in this academic racing game. Take a spin on the virtual track and after that complete versus a highlighted AWS DeepRacer self-governing model in this arcade racing experience, with prizes and free gifts galore. Shift into the fast lane on your ML learning journey with AWS DeepRacer.
Head over to the re: Invent website to develop your schedule so youre ready to hit the ground running. Be sure to come by and speak with our experts at the AI/ML booth, or chat with the speakers after sessions. We cant wait to see you in Las Vegas!

Back in individual, we now have chalk talks, workshops, builders sessions, and our conventional breakout sessions. There are over 200 AI/ML sessions, including breakout sessions with clients such as Aon Corporation, Qualtrics, Shutterstock, and Bloomberg.
Join this session to discover how to prepare information for ML in minutes using Amazon SageMaker. In this session, find out about the implementation alternatives available in Amazon SageMaker, consisting of optimized infrastructure options; real-time, asynchronous, and batch inferences; multi-container endpoints; multi-model endpoints; automobile scaling; design tracking; and CI/CD combination for your ML work. During this session, check out the breadth of MLOps features in Amazon SageMaker that help you arrangement consistent design advancement environments, automate ML workflows, execute CI/CD pipelines for ML, display designs in production, and standardize model governance abilities.

About the Authors.
Andrea Youmans is a Product Marketing Manager on the AI Services team at AWS. Over the past 10 years she has actually worked in the innovation and telecom markets, focused on developer storytelling and marketing campaigns. In her spare time, she delights in heading to the lake with her husband and Aussie pet Oakley, tasting wine and enjoying a motion picture from time to time.

Leave a Reply

Your email address will not be published.