AWS Machine Learning Summit 2021
2021-06-02
-- online --
as the organisers put it
On June 2, 2021, don’t miss the opportunity to hear from some of the brightest minds in machine learning (ML) at the free virtual AWS Machine Learning Summit. Machine learning is one of the most disruptive technologies we will encounter in our generation. It’s improving customer experience, creating more efficiencies in operations, and spurring new […]
| title | author | length |
|---|---|---|
| Opening Keynote | Swami Sivasubramanian, Bratin Saha, Yoelle Maarek | 01h 00m |
Overview of concrete claims, tools and results: AWS reports shipping Few‑Shot Learning into Rekognition and Lookout for Vision so customers train with 10… | ||
| Fireside Chat | Andrew Ng, Swami Sivasubramanian | 27m |
Conversation argues that the core claim is practical: most ML projects stall on the proof-of-concept-to-production gap and the fix is concrete — ship a quick… | ||
| Spin up Jupyter notebooks at scale and enhance your productivity | Brian E. Granger | 32m |
Demonstrates that Amazon SageMaker Studio runs JupyterLab-based notebooks at scale by launching per-notebook compute that users can switch without managing… | ||
| Choose the right algorithm to help you build better ML models | Denis V. Batalov, PhD | 32m |
Amazon SageMaker demonstrates choosing from 17 built‑in algorithms and shows k‑NN production techniques using the Faiss library for indexing, Faiss IVF flat… | ||
| Prepare data for machine learning with ease, speed, and accuracy | Mark Roy | 34m |
Demonstrates a concrete workflow: use SageMaker Data Wrangler to load ~2 million CSV rows with 26 columns, run visualizations and 300+ built-in plus custom… | ||
| Tune your ML models to the highest accuracy with automatic model tuning | Emily Webber | 33m |
Amazon SageMaker automatically tunes hyperparameters using Bayesian optimization by launching many child training jobs from a provided estimator, evaluating an… | ||
| Gain better insights while training ML models with SageMaker Debugger | Nathalie Rauschmayr | 28m |
SageMaker Debugger captures weights, gradients and activations with zero-code change and uploads snapshots (default every 500 steps) to S3 while a separate… | ||
| Understand your ML models better with greater visibility and transparency | Pinar Yilmaz | 18m |
Hands-on demo of Amazon SageMaker Clarify shows concrete outputs: pre/post-training bias reports (JSON, HTML, PDF) from a credit dataset with age > 40 as a… | ||
| Accelerate deep learning in the cloud with custom ML environments | Shashank Prasanna | 35m |
Shows how to go from an EC2 Jupyter notebook to reproducible containers by using Deep Learning AMIs as bases, writing a Dockerfile that copies a CIFAR training… | ||
| Accelerate NLP training with Amazon SageMaker | Aditya Bindal | 25m |
Amazon SageMaker adds two libraries—SMdistributed.dataparallel and SMdistributed.modelparallel—that require minimal code changes and claim 20–40% faster… | ||
| Cultivating a company-wide machine learning culture | David Frazee | 30m |
3M shows concrete wins: CloudMaker uses factory sensor data to predict web breaks; a polymer plant runs an RNN on S3/EC2 with SageMaker (TensorFlow/PyTorch) to… | ||
| Transforming pharmaceutical R&D through industrialized machine learning | Ian Dix, Rui Wang | 27m |
AstraZeneca builds an AWS-based ML platform that runs hundreds of projects with hundreds of active users, cutting environment setup from months to days. A… | ||
| is designing ML solutions to deliver human capital insights | Jack Berkowitz | 30m |
ADP turns 90 million anonymized employee records into production ML: a skills graph stored in Amazon Neptune, 20 SageMaker-hosted models, and APIs that hit… | ||
| is delighting fans with game-changing insights | Andreas Heyden | 18m |
Bundesliga builds real‑time “Match Facts” from stadium camera and tracking feeds, turning a 150,000‑hour archive and live 3.6 million per‑match data points… | ||
| is modernizing financial services through automation | Ritesh Shah | 29m |
Vanguard describes a centralized ML platform built on Amazon SageMaker with a conversational setup flow (Amazon Lex UI on Fargate, Okta/Cognito SSO) that… | ||
| Fighting climate change at the edge | Brenden Millstein | 10m |
Carbon Lighthouse runs a CLUES AI platform that streams sensor data from mailed sensor kits into AWS ( EC2 RDS S3 DynamoDB ) to run tens of thousands of… | ||
| Increase productivity and satisfaction with an intelligent contact center | Shanthan Kesharaju, Dave Rennyson | 32m |
A blueprint for adding ML to contact centers with concrete results: uses Amazon Connect, Amazon Lex, Polly, Transcribe, Comprehend, Kendra and SageMaker to… | ||
| Get Rolling on Machine Learning | Naomi Teng | 24m |
Hands-on walkthrough of building and training an AWS DeepRacer model using AWS RoboMaker for 3D simulation and Amazon SageMaker for training, with experiences… | ||
| Transform your business with data-driven insights | Karl Albertsen | 25m |
Demonstrates three AWS services with concrete results: Amazon Forecast powers SKU-level time-series forecasting and weather-aware features yielding a 30%… | ||
| Bring AutoML to all users across your business | Kate Zimmerman | 30m |
AWS presents AutoML embedded across the stack with over 70 AutoML-enabled services and two hands-on demos: Rekognition Custom Labels fine-tunes a vision model… | ||
| Maximize lifetime value of media content | Esther Lee, James Jameson | 33m |
Demonstrates a working pipeline that extracts millisecond metadata with Amazon Transcribe plus Rekognition Comprehend and Translate to enable search… | ||
| Automate your document pipeline with intelligent document processing | Prem Ranga | 35m |
Step-by-step demo builds a serverless pipeline that extracts text with Amazon Textract, runs a custom entity recognizer using Amazon Comprehend (trained on two… | ||
| Give users relevant answers quickly with intelligent search | Sara van de Moosdijk | 28m |
Amazon Kendra indexes diverse sources (50+ native connectors or custom connectors) and returns extractive answers plus document lists, with relevance tuned by… | ||
| Transforming DevOps at Fidelity Investments with intelligent insights | Jacob Sullivan, Keith Blizard | 30m |
Fidelity uses Amazon DevOps Guru to auto-ingest CloudWatch, CloudTrail, AWS Config and X-Ray data, run ~15 pretrained ML detectors that calibrate over 14 days… | ||
| Transform online shopping experiences with personalized recommendations | Sarah Strobhar | 27m |
Shows how Amazon Personalize ingests interaction, item and user metadata, uses a real-time HRNN + contextual bandit stack with impression modeling and dynamic… | ||
| Causality, robustness, and natural language understanding in ML | Dilek Hakkani-Tur, Alex Smola, Bernhard Schöelkopf | 34m |
Argues that recent gains come from huge web text and models trained at scale — compute up 3–4 orders of magnitude — but current systems still mostly… | ||
| The ethical algorithm | Michael Kearns | 27m |
Shows that common anonymization breaks via linkage attacks and cites the 2010 law‑review paper, then advocates differential privacy implemented by adding… | ||
| Towards controllable language generation | Kathleen McKeown | 26m |
Deep learning models regain controllability through concrete methods: using noise injection plus self‑training on the E2E data yields a new seq2seq “faithful… | ||
| Analyzing social media for suicide risk using natural language processing | Philip Resnik | 32m |
Researchers apply NLP and machine learning to social media and clinical text, build a donation platform (Our.Data.Helps.org) and a university mirror… | ||
| Building high-quality computer vision models using only a few examples | Marzia Polito | 23m |
Demo shows training a single-object detector from 21 pizza images with drawn bounding boxes and automatic hyperparameter selection. Production deployment… | ||
| Deep Graph Library: Deep Graph learning at scale | George Karypis | 26m |
DGL 0.6 is an open-source graph neural network library built on PyTorch/MXNet/TensorFlow with domain packages like DGL-LifeSci and DGL-KG, implements optimized… | ||
| COVIDcast: An ecosystem for COVID-19 tracking and forecasting | Ryan Tibshirani | 33m |
An open COVIDcast Indicator Database ingests partner data from Facebook surveys, Change Healthcare claims, Quidel antigen tests, Google searches and… | ||
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