Graph Neural Networks, the new way of doing Deep Learning

Most data in the real world is non-Euclidean, meaning they are not in tabular form. By squashing the data in a table, we loose invaluable information, especially in terms of structural similarities and causal relatedness. Graph Neural networks embrace non-tabular data. In the last few years, both science and technology have advanced to finally allow massive parallel computations on Graphs and open up a new frontier on deep learning. By attending this talk, you will learn about graph neural networks and an example of how to easily train a graph neural network using Neptune ML.

Speaker: Will Badr, Principal SA, AWS

No code machine learning with Amazon SageMaker Canvas

There is often a treasure trove of internal data available to data modelers, data engineers, data analysts in the enterprises. Citizen data experts in those companies are often not data scientists and they can only utilize their data for reactive analytical reporting. In this session, you learn how Amazon Canvas provides a visual no-code tool for predictive analytics.

Speaker: Sofian Hamiti, Senior ML Solutions Architect, AWS

ML the easy way Free of charge ML sandbox with Amazon SageMaker Studio Lab

In a fast moving field such as machine learning (ML), continuous learning is of essential value. Whether data scientists and researchers are trying to learn new subjects or the novice trying to dip their toe in the field for the first time, they would need an environment that is free of charge, free of complexity to consume services, and free of stress and hassle of having to use credit cards to log in and making mistakes that result in payable bills. Amazon SageMaker Labs provide such open environments in which an experimenter can train models on GPU instances without having to pay or use a credit card as a guarantee. Additionally, the Amazon SageMaker Labs provides connectivity to other fundamental AWS services to provide experimenters with the ability to test their work against similar environments to those they use at work. This talk intends to provide an introductory hands on guide to using Amazon SageMaker Labs.

Speaker: Boaz Ziniman, Developer Advocate, AWS

Train your models faster using Amazon SageMaker training compiler

As DL models grow in complexity, so does the time it can take to optimize and train them. In practice, optimizing machine learning (ML) code is difficult, time-consuming, and a rare skill set to acquire. Data scientists typically write their training code in a Python-based ML framework and rely on those frameworks to compile their models to efficient kernels. These translations are often very inefficient. In this session you will learn how to use SageMaker Training Compiler to automatically compile your Python training code and generate GPU kernels specifically for your model.

Speaker: Gili Nachum, AI/ML Specialist Solutions Architect, AWS

Create High Quality Datasets using Amazon SageMaker GroundTruth Plus

Most machine learning models in production today are based on supervised learning. High quality labeled data is in the core of supervised learning. The challenge is that despite massive growth in the volume of available data, high quality labeled data is still a rare commodity. Amazon SageMaker Ground Truth Plus enables you to easily create high-quality training datasets without having to build labeling applications and manage the labeling workforce on your own. By attending this talk, you will have a hands-on introduction to Automated data labeling using Amazon Ground Truth Plus.

Speaker: Sohan Maheshwar, Senior Developer Advocate, AWS

Large Scale data processing with EMR and Spark using Amazon Sagemaker Studio

Large scale training requires data streaming and data processing at massive scale. Often Apache Spark is in the center of such data pipeline. Apache Spark, Apache Hive, and Presto running on Amazon EMR clusters directly from Amazon SageMaker Studio notebooks to run petabyte-scale data analytics and machine learning. This talk provides a hands-on introduction in using Apache Spark from within SageMaker notebooks.

Speaker: Jon Reade, Senior AI/ML Specialist Solutions Architect, AWS

Prepare and make sense of your data rapidly using Amazon SageMaker Data Wrangler

The process of data preparation and feature engineering is perhaps the most tedius and the most time consuming part of machine learning. The challenge that begins with time and resources for the preparation phase extends to lengthy time to market and uncertainty about whether or not, data processing and feature engineering processes have led to an optimal input to the models. Amazon SageMaker Data Wrangler supports optimization and automation of data processing and feature engineering using visual tools. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. This talks provides data engineers with a practical guide to Amazon SageMaker Data Wrangler.

Speaker: Javier Ramirez, Developer Advocate, AWS

A Low Code solution to prepare, analyze, and view your data

Data preparation is a critical step to get data ready for analytics or machine learning. As data continues to grow in size and complexity, you need to expand the number of people preparing and unlocking value in your data. In this session, dive deep into AWS Glue DataBrew, a new visual data preparation tool that enables data analysts and data scientists to clean and normalize data without writing code. When attending this session, you will see a walkthrough of how AWS Glue DataBrew works, popular use cases, and best practices for data preparation across all your data stores.

Speaker: Yonatan Dolan, Analytics Specialist, AWS

Automate code reviews using Amazon CodeGuru Reviewer

Working under pressure and against deadlines can result in cutting corners in code quality. In most extreme ends of the spectrum, hard coded secrets and passwords that were used for debugging purposes, could be left out in the code. Amazon CodeGuru provides intelligent recommendations to improve code quality and identify an application's most expensive lines of code. Amazon CodeGuru security detectors use machine learning and automated reasoning to analyze data flow to perform whole-program inter-procedural analysis, across classes, methods, and files to detect hard-to-find security vulnerabilities. This talk will provide machine learning developers a hands-on insight into using CodeGuru in general and use of CodeGuru Reviewer in particular.

Speaker: Ana Cunha, Developer Advocate, AWS

Analyze Existing Sensor Data to Detect Abnormal Equipment Behavior

Successfully implementing predictive maintenance requires using the specific data collected from all of your machine sensors, under your unique operating conditions, and then applying machine learning (ML) to enable highly accurate predictions. However, implementing an ML solution for your equipment can be difficult and time-consuming. In this session, you will learn about Amazon Lookout for Equipment, which allows you to analyze the data from the sensors on your equipment to automatically train a ML model based on your equipment data – with no ML experience required.

Speaker: Sean Tracey, Senior Developer Advocate - UK&I, AWS

Extract data and insights from your documents

Organizations across all industries are still manually processing their documents which is time consuming, prone to error, and costly. Learn how machine learning can automate document processing extracting data and insights from insurance claims, mortgage applications, healthcare claims or legal contracts among others.

Speaker: Mia Chang, ML Specialist Solutions Architect, AWS

Create real-time personalized user experiences faster at scale

You are willing to deliver the best possible experience to your customers. Business is asking developers to build applications capable of delivering a wide array of personalization experiences, including specific product recommendations, personalized product ranking, similar items recommendations, and customized direct marketing. In this session, learn how to use Amazon Personalization to import your data, pick your use case and in a few easy steps, spin up a machine learning recommender ready to integrate into your applications.

Speaker: Anna Gruebler, Senior AI Specialist SA, AWS

Reduce Model Deployment time using Amazon SageMaker Inference Recommender

Choosing what sort of inference instance to pick for a model is hard and requires extensive tests and sizing exercises, all of which is time consuming and expensive. Amazon SageMaker Inference Recommender automates this process, speeding up the deployment and reducing inference costs. In this talk we intend to provide a practical guide to setting up and using Amazon SageMaker Inference Recomender.

Speaker: Mohammed Fazalullah Qudrath, Senior Developer Advocate - MENA, AWS

Load-test your inference endpoints

Serverless endpoints are a popular choice for most use cases. They are specifically effective for unpredictable and spiky traffic. A well planned autoscaling is in the core of operational excellence for spiky systems. Load testing is one of the major methods in the reretior of Ops teams to plan their autoscaling. This talk provides a hands-on guide to load test Amazon SageMaker endpoints using artillery and serverless framework.

Speaker: Cyrus Vahid, Principal ML Specialist Developer Advocate, AWS

Implementing ML Ops practices with Amazon SageMaker

This session is focused on providing a practical guide to prepare, build, train, deploy, and manage models at scale. The sesson provides a guide to provision consistent model development environments, automate machine learning (ML) workflows, implementing CI/CD pipelines for ML, monitoring models in production, and standardizing model governance capabilities.

Speaker: Giuseppe Angelo Porcelli, Principal - AI/ML Specialist SA, AWS

Run and manage your ML models at the edge with SageMaker Edge

Edge devices often run applications that need to make autonomous decisions at low latency with incoming data feeds from cameras, robots and other physical sensors. The devices may be located in remote locations with limited cloud connectivity or may have strict data privacy and regulatory requirements. Amazon SageMaker Edge enables machine learning (ML) on edge devices by optimizing, securing, and deploying models to the edge. This talk will provide the audience a hands on introduction to ML inference at the edge and the cloud to edge lifecycle.

Speaker: Hasan Poonawala, Senior AI/ML Specialist Solutions Architect, AWS

What can AIML and Analytics do for Industrial sector companies ? (automotive, manufacturing, energy)

Industrial companies are leveraging machine learning (ML) and Analytics to increase efficiency, performance, and sustainability across every aspect of their life cycle, from product design to after-sales services, connecting the shop-floor through IoT services, using terabytes of telemetry data from millions of sensors, improving operational performance and predicting failures. Learn from companies across automotive, energy and manufacturing sectors including BMW, Volswagen, MobilEye, Engie, Octopus Energy, Enel, Shell, BP, Siemens and SKF.

Speaker: Lionel Billon, Head of Analytics & ML - EMEA South & Emerging Markets, AWS

What can AIML and Analytics do for Financial Services companies?

Financial institutions are using machine learning (ML) and Analytics extensively to identifies fraudulent transactions, bring new services to customers (e.g. loans), maintain compliance with strict financial service standards, address cybersecurity attacks, or improve customer service. Learn how to build an FSI ML environment from leading financial companies such as AXA, Zopa, Barclays, Euler Hermes, Siemens Finance, Nat West, and Ergo/Munich Re.

Speaker: Dimitri French, Principal - Machine Learning, AWS

What can AIML and Analytics do for Pharma and Life Science companies?

Using machine learning (ML) and Analytics in pharma is revolutionizing the drug discovery process, accelerating experimentation cycles, bringing drugs to market faster, and optimizing logistics, with for example for the development of the new mRNA COVID-19 vaccine. Learn from leading Pharma companies such as AstraZenecca, BioNTech, Moderna, Novartis, Roche or Merk how they use ML to innovate and faster deliver to market.

Speaker: Luis Campos, EMEA Data, Analytics & AI/ML Modernisation Lead, AWS

What can Machine Learning do for Software and Internet companies?

Creating machine learning (ML) powered products is the driving engine of leading Software and Internet companies. Learn how entrepreneurs and product managers leverage ML technology to build market leading products. From use case qualification, through ascertaining data quality, to monitoring concept drifts. We will double click on what makes successful ML powered products in Software and Internet companies such as Wix, Amdocs, Gong, d.velop, EXASOL, or Bolt.

Speaker: Oren Steinberg, Head of AI & ML in North and South EMEA, AWS

Rethink possible: Innovation stories

Discover the technology innovation stories which has helped capture new opportunities, grow revenue, and solve the big problems faced today and in the future. Join this session and uncover how machine learning, supercomputing, AI, and robotics are powering manufacturing in space, enabling the growth of "super dust rice" to feed the world’s growing population, helping us build the first human base camp on the moon, creating a new era of FORMULA 1 racing, and fighting climate change.

Hosts:
Olivier Klein, Chief Technologist for APJ, AWS
Dr. Michelle Dickinson, Nanotechnologist and Materials Engineer

Customer speaker: Rob Smedley, Director of Data Systems, F1
Learn how F1 used computational fluid dynamics to build a new F1 car design that enables closer, more exciting, wheel-to-wheel racing, a process that would have taken more than 470 years on a standard laptop.

Customer speaker: Tatiana Calderón, Alfa Romeo Racing ORLEN Test Driver and Team Ambassador, F1
Hear the driver’s perspective on the new style of racing the 2022 F1 car will enable and what these changes mean for F1 drivers and fans in the future.

Customer speaker: Dr. Jordan Nguyen, Biomedical Engineer & Technology Futurist
Preventing age-related disease, increasing food production, and conserving wildlife remain some of the biggest challenges we face today. Learn how advances in genomics research, powered by cloud, AI, and supercomputing, help us grow “super dust rice” in desert conditions, protect the Amur tiger from extinction, and eradicate age-related disease.

Customer speaker: Dr. James Kuffner, Representative Director and CEO, Woven Planet Holdings
Toyota’s Woven City project is a purpose-built city of the future being built at the foot of Mount Fuji. Designed using digital twin technology, the city will weave together leading-edge mobility and communications technology with green spaces and sustainable infrastructure.

Customer speaker: Andrew Rush, President and COO, Redwire
Redwire uses 3D printing, robotics, artificial intelligence, and machine learning, to manufacture satellites, structures, and products in the unique zero-gravity environment of space. This vision is advancing space exploration and creating better products for life on Earth.

Customer speaker: Sally Fouts, Director of The Climate Pledge, Amazon
The Climate Pledge is a commitment to be net zero carbon by 2040, which is ten years ahead of the Paris Agreement. Learn how the pledge encourages companies to join Amazon in taking climate action and accelerating goals, plans, and programs to address the urgency of climate change.

Customer speaker: Nujoud Merancy, Chief, Exploration Mission Planning Office, NASA
Discover the technology innovation helping NASA build the first long-term basecamp on the Moon, and send astronauts to Mars.

Customer speaker: Glenn Gore, CEO, Affinidi
Affinidi uses Web 3.0 to power self-sovereign identity solutions, with personal data owned by the individual, creating new business models worldwide.

Speaker: Tom Soderstrom, Chief Technologist, Worldwide Public Sector, AWS
Advances in technology combined with lower costs are powering the growth of the space economy through innovative projects to protect earth from space, improve sustainability in space, and open a new era in space exploration.