intel logo

Level up your machine learning and AI skills!

Welcome to AWS Innovate Online Conference – Machine Learning & AI Edition, a free virtual event designed for developers, data scientists, IT professionals, and data engineers who are looking to bring new ideas to reality. Whether you are new to machine learning or an advanced user, AWS Innovate has the right sessions for you to level up your skills.

Hear the very latest from Julien Simon, Principal Evangelist for AI & Machine Learning, AWS, during the opening keynote and closing remarks. Dive deep into any of the 20+ sessions across five tracks. AWS technical experts will explain key features and use cases, share best practices, walk through technical demos, and be available to answer your questions one-on-one via live Q&A. 

Why Attend
Why Attend
This free event is designed to teach you how to build, train, and deploy sophisticated models within any framework, build the next generation of intelligent apps, and much more! Discover how AI/ML knowledge can transform not only your data models but also your business and career.
Who Should Attend
Who Should Attend
Whether you are new to machine learning or an advanced user, this event has a specific track for your level of experience & job role. This event is ideal for application developers, data scientists & researchers, IT professionals, engineers, architects & those working in DevOps.
modulesfinal

            20+ SESSIONS              

Q&A

LIVE Q&A

live-demos

    TECHNICAL DEMOS  

certificateawsomeday

CERTIFICATE OF ATTENDANCE

Event Agenda

Click here to download agenda

10:00 AM
London time
40 min.

Opening Keynote by Julien Simon, Principal Technical Evangelist for AI & Machine Learning, AWS

Ask the experts

Track

I'm starting my career in machine learning

I'm a data scientist or researcher

I'm in DevOps working with data scientists & researchers

I'm an application developer

I’m a developer who wants to get hands on (Twitch)

Open throughout the conference

(10:00 AM – 2:30 PM London time)

10:45 AM

40 min.

Introduction to machine learning with Python and scikit-learn

(Level 200)

Scale machine learning from zero to millions of users

(Level 200)

AWS data services for machine learning

(Level 200)

AWS AI services for image and video analysis

(Level 200)

Getting started with AWS DeepRacer

(Level 200)

11:30 AM

40 min.

Getting started with machine learning using Amazon SageMaker

(Level 200)

Build, train, and deploy machine learning models with Amazon SageMaker

(Level 300)

Automating Amazon SageMaker workflows

(Level 200)

Breaking language barriers with AI

(Level 300)

Advanced topics with AWS DeepRacer

(Level 300)

12:20 PM

40 min.

Introduction to deep learning

(Level 200)

Deep dive on Amazon SageMaker

(Level 400)

Building a secure data science environment

(Level 200)

Build conversational interfaces for your customers

(Level 200)

AWS DeepLens

(Level 300)

1:05 PM

40 min.

Getting started with deep learning using Amazon SageMaker

(Level 200)

Migrating your machine learning workloads to Amazon SageMaker

(Level 300)

Combining Apache Spark and Amazon SageMaker

(Level 300)

Simplifying time-series forecasting and real-time personalization

(Level 300)

1:50 PM

40 min.

Closing Remarks by Julien Simon and Pavlos Mitsoulis-Ntompos, AWS ML Hero

Session Descriptions

  • Opening Keynote
  • I'm starting my career in machine learning
  • I'm a data scientist or researcher
  • I'm in DevOps working with data scientists and researchers
  • I'm an application developer
  • I’m a developer who wants to get hands on (Twitch)
  • Closing Remarks
  • Opening Keynote
  • Opening Keynote

    In the opening keynote, Julien Simon, Principal Technical Evangelist for AI & Machine Learning at AWS, provides an overview of the AWS AI/ML service portfolio as well as key use cases and customer experiences across different industries—along with recent features that you might have missed.

    Speakers:
    Julien Simon, Principal Technical Evangelist for AI & Machine Learning, AWS

  • I'm starting my career in machine learning
  • Introduction to machine learning with Python and scikit-learn (Level 200)

    Scikit-learn is a machine learning library for Python that implements many common ML algorithms while offering a uniform programming interface. This session guides you through core ML concepts and scikit-learn algorithms to help you get started from scratch and includes hands-on demos.

    Speaker: Alex Casalboni, Technical Evangelist, AWS

    Getting started with machine learning on Amazon SageMaker (Level 200)

    Amazon SageMaker is a fully managed, modular service that enables developers and data scientists to build and scale machine learning solutions. Using Jupyter notebooks, we show you how to build, train, and deploy models using built-in algorithms (such as XGBoost) as well as the popular scikit-learn library.

    Speaker: Martin Beeby, Technical Evangelist, AWS

    Introduction to deep learning (Level 200)

    The arcane jargon and intimidating equations of deep learning often discourage software developers, who wrongly think that they’re “not smart enough,” from pursuing it. In this session, we introduce you to deep learning theory (training, optimization, etc.) with minimal math. Then we give you an overview of different architectures (MLP, CNN, LSTM, GAN) and the kinds of problems they can help you solve.

    Speaker: Adrian Hornsby, Senior Technical Evangelist, AWS

    Getting started with deep learning on Amazon SageMaker (Level 200)

    Amazon SageMaker is a fully managed platform that enables developers and data scientists to build and scale machine learning solutions. Using Jupyter notebooks, we show you how to build, train, and deploy models using deep learning frameworks (TensorFlow, Apache MXNet, etc.). You also learn how to easily move your existing deep learning code to Amazon SageMaker with minimal modifications.

    Speaker: Antje Barth, Technical Evangelist for AI & Machine Learning, AWS

  • I'm a data scientist or researcher
  • Scale machine learning from zero to millions of users (Level 200)

    Data scientists and machine learning engineers use a variety of tools that make it easy to start everyday tasks. But as models become more complex and datasets become larger, training time and prediction latency become a significant concern. In this session, we show you how to scale machine learning workloads using AWS services (AWS Deep Learning AMI and containers, Amazon ECS, Amazon EKS, AWS Fargate). We also discuss the relative advantages of these different services as well as run some interesting demos.

    Speaker: Sébastien Stormacq, Technical Evangelist, AWS

    Build, train, and deploy machine learning models with Amazon SageMaker (Level 300)

    Amazon SageMaker is a fully managed, modular service that enables developers and data scientists to build and scale machine learning solutions. In this session, we first show you how Amazon SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we show you how to build, train, and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc.).

    Speaker: Julien Simon, Principal Technical Evangelist for AI & Machine Learning, AWS

    Deep dive on Amazon SageMaker (Level 400)

    This session expands on the previous one and dives deep on advanced features available in Amazon SageMaker for training and inference, such as pipe mode, distributed training, managed spot training, automatic model tuning, model compilation with Amazon SageMaker Neo, and Amazon Elastic Inference.

    Speaker: Julien Simon, Principal Technical Evangelist for AI & Machine Learning, AWS

    Migrating your machine learning workloads to Amazon SageMaker (Level 300)

    With Amazon SageMaker, you can choose to build, train, and deploy models using the pre-built algorithms, employing popular frameworks such as Keras, MXNet, TensorFlow, PyTorch, and Spark ML, or even bringing your own algorithms using Docker containers. With live demos, this session shows you how to bring your model created elsewhere, or your own custom algorithm, into Amazon SageMaker.

    Speaker: Javier Ramírez, Technical Evangelist, AWS

  • I'm in DevOps working with data scientists and researchers
  • AWS data services for machine learning (Level 200)

    Modern data is massive, quickly evolving, unstructured, and—coming from multiple consumers and applications—increasingly hard to catalog and understand. This session guides you through the best practices for designing a robust data architecture, highlighting the benefits and typical challenges of data lakes and data warehouses. In particular, we build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.

    Speaker: Alex Casalboni, Technical Evangelist, AWS

    Automating Amazon SageMaker workflows (Level 200)

    In this session, we show you how to automate the training and deployment of ML models built with Amazon SageMaker. With live demos of different tools (boto3, AWS CloudFormation, AWS Lambda, AWS Step Functions), we specifically show you how to automate training, retraining, canary deployment, blue-green deployment, etc.

    Speaker: Cobus Bernard, Technical Evangelist, AWS

    Building a secure data science environment (Level 200)

    Customers are building secure environments in the cloud for data science, analytics, and machine learning. In this session, we introduce the services and features that customers are using to build secure data science environments. The session focuses on Amazon SageMaker, a fully managed service supporting the entire machine learning lifecycle, and touches on supporting AWS services that enable you to secure your machine learning workloads in the AWS Cloud.

    Speaker: Jason Barto, Solutions Architect, AWS

    Combining Apache Spark and Amazon SageMaker (Level 300)

    Amazon SageMaker is a fully managed, modular service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker also provides an Apache Spark library, in both Python and Scala, that you can use to easily train models in Amazon SageMaker from your Spark clusters. Further, after a model has been trained, you can deploy it using Amazon SageMaker hosting services. After a brief recap of Apache Spark and Amazon SageMaker, this webinar shows you how to integrate Amazon SageMaker with your Apache Spark application (including how to start training jobs and use them in Spark pipelines).

    Speaker: Frank Munz, Technical Evangelist, AWS

  • I'm an application developer
  • AWS AI services for image and video analysis (Level 200) 

    Humans can look at an image or video and identify objects, recognize faces, and extract information. After years of research, computers are now also able to perform these tasks, potentially leading to numerous business benefits. In this session, we show you how to extract information from images and video using a pre-trained service called Amazon Rekognition—and with demos and code examples, we show you how to add image and video analysis to your applications. No machine learning skill is required.

    Speaker: Martin Beeby, Technical Evangelist, AWS

    Breaking language barriers with AI (Level 300)

    Amazon brings natural language processing, speech recognition, text to speech, and machine translation within the reach of every developer. API-driven application services enable developers to easily plug pre-built artificial intelligence functionality into their applications and to automate workflows. In this session, we share how to build the next generation of intelligent apps that hear, speak, and understand the world around us.

    Speaker: Boaz Ziniman, Technical Evangelist, AWS

    Build conversational interfaces for your customers (Level 200)

    Talking and listening is the most natural way to interact; we learn to do so from the day we are born. In this session, we cover how to build great conversational UI to delight your customers. We also cover the basics of speech recognition and natural language processing as well as explore the main programming interface, and we illustrate these concepts with Amazon Lex and Amazon Polly.

    Speaker: Sébastien Stormacq, Technical Evangelist, AWS

    Simplifying time-series forecasting and real-time personalization (Level 300)

    Personalization and forecasting have long been very complex problems for organizations to solve. In this session, we show you how to use Amazon Personalize and Amazon Forecast, two new services that enable you to create individualized recommendations for customers and deliver highly accurate forecasts. Both run on fully managed infrastructure and provide easy-to-use recipes that deliver high-quality models even if you have little machine learning experience.

    Speaker: Danilo Poccia, Principal Evangelist, AWS

  • I’m a developer who wants to get hands on (Twitch)
  • Getting started with AWS DeepRacer (Level 200)

    Developers, start your engines! Whether you are new to machine learning or ready to build on your existing skills, we can help you get ready to race. In this tech talk, we show developers, even those with no prior machine learning experience, how to get started with the basics of reinforcement learning (a branch of machine learning that's ideal for training autonomous vehicles) and prepare them to compete in the AWS DeepRacer League.

    Speaker: Ricardo Sueiras, Technical Evangelist, AWS

    Advanced topics with AWS DeepRacer (Level 300)

    In this session, we get under the hood and learn how to use services such as Amazon SageMaker and tools such as the log analyzer to help you tune your reward functions and get the best performance from your models. We are joined by a prominent member of the independent AWS DeepRacer community, who shares additional tips on how to maximize your models’ performance.

    Speakers:

    • Ricardo Sueiras, Technical Evangelist, AWS
    • Lyndon Leggate, AWS Machine Learning Hero

    AWS DeepLens (Level 300)

    AWS DeepLens helps put machine learning in the hands of developers—literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills. In this session, we show you how to quickly get started with DeepLens—using built-in projects at first, and then creating a custom project using a computer vision model trained in Amazon SageMaker.

    Speaker: Julien Simon, Principal Technical Evangelist for AI & Machine Learning, AWS

  • Closing Remarks
  • Closing Remarks

    Julien Simon and Pavlos Mitsoulis-Ntompos, Staff Data Scientist at Expedia Group as well as AWS Machine Learning Hero, take the stage for closing remarks. How to get started with ML, even if you don’t have formal training? How to make the most of AWS services? How does Amazon SageMaker help ML developers? These questions and more will be answered. Pavlos also demos Sagify, his popular open-source command line utility for Amazon SageMaker.

    Speakers:

    • Julien Simon, Principal Technical Evangelist for AI & Machine Learning, AWS
    • Pavlos Mitsoulis-Ntompos, Staff Data Scientist, Expedia Group, and AWS Machine Learning Hero
Level 100
Introductory
Sessions are focused on providing an overview of AWS services and features, with the assumption that attendees are new to the topic.
Level 200
Intermediate
Sessions are focused on providing best practices, details of service features, and demos, with the assumption that attendees have introductory knowledge of the topics.
Level 300
Advanced
Sessions dive deeper into the selected topic. Presenters assume that attendees have some familiarity with the topic, but may or may not have direct experience implementing a solution on their own.
Level 400
Expert
Sessions are for attendees who are deeply familiar with the topic, have implemented a solution on their own already, and are comfortable with how the technology works across multiple services, architectures, and implementations.

Featured Speakers

Swami Sivasubramanian, VP Amazon Machine Learning, AWS
Julien Simon, Principal Technical Evangelist for AI & Machine Learning, AWS

As a global AI & machine learning evangelist, Julien focuses on helping developers and enterprises bring their ideas to life. He frequently speaks at conferences, and he blogs at https://medium.com/@julsimon. Prior to joining AWS, Julien served for 10 years as CTO/VP of engineering in top-tier web startups, where he led large software and ops teams with responsibility for thousands of servers worldwide. In the process, he fought his way through a wide range of technical, business, and procurement issues, which helped him gain a deep understanding of physical infrastructure, its limitations, and how cloud computing can help. Julien holds ten AWS certifications.

Olivier Klein, Head of Emerging Technologies, AWS
Danilo Poccia, Principal Technical Evangelist, AWS

Danilo works with companies of all sizes—and startups in particular—to support their innovation initiatives. In his role as an evangelist at Amazon Web Services, he leverages his experience to help people bring their ideas to life, focusing on serverless architectures and event-driven programming, and on the technical and business impact of machine learning and edge computing. He is the author of the book AWS Lambda in Action from Manning Publications.

Dean Samuels, Solution Architect Manager, AWS
Adrian Hornsby, Senior Technical Evangelist, AWS

Adrian is a senior technical evangelist at Amazon Web Services and is based in the Nordics. He has over 15 years of experience in the IT industry, having worked as a software and systems engineer; a backend, web, and mobile developer; and part of DevOps teams where his focus has been on cloud infrastructure and site reliability, writing application software, deploying servers, and managing large-scale architectures. The truth is that Adrian loves breaking stuff—controlled chaos is his thing. Adrian frequently speaks at conferences and community meetups, and he blogs at https://medium.com/@adhorn.

Frequently Asked Questions

Q: Where is AWS Innovate hosted?

AWS Innovate is an online conference. After completing the online registration process, you will receive a confirmation email containing the login link that you will need in order to access the platform.

  • London Timing: 10:00am UTC +1
  • Paris Timing: 11:00am UTC +2
  • Berlin Timing: 11:00am UTC +2
  • Cape Town Timing: 11:00am UTC +2
  • Kiev Timing: 12:00pm UTC +3

Q: Can I watch the sessions after the event?
All sessions will be available on demand from the day after the event until the end of November.

Q: Who should attend AWS Innovate?
Whether you are new to machine learning or an advanced user, this event has a specific track for your level of experience & job role. This event is ideal for application developers, data scientists & researchers, IT professionals, engineers, architects & those working in DevOps.

Q: What is the price to attend AWS Innovate?
AWS Innovate is a free online conference.

Q: Can I get a confirmation of my AWS Innovate registration?
After completing the online registration process, you will receive a confirmation email.

Q: How do I get the certificate of attendance?
If you watch four complete sessions during the live conference, we will send a certificate of attendance one week after the conference has ended, to the email you used when registering for the conference.

Q: How can I get $25 AWS Credits?
If you tune in on October 17 and submit the feedback form, you are eligible for $25 AWS Credits. Double your credits by becoming an Innovate Champion (watch 4 sessions, and submit 4 polls & the feedback form during the live event). Please read the AWS Promotional Credit Terms and Conditions here.

Eligible attendees will receive their credit codes via email by November 30.

Q: How can I contact the online conference organizers?
If you have questions that have not been answered in the FAQs above, please email us here.

Get started with Machine Learning for free

Create your free AWS account and get started with free offers and services to build, deploy, and run machine learning applications in the cloud.
signup-icon

Sign up for an AWS account

Creating an AWS account is free and gives you immediate access to the AWS Free Tier
tutorial-icon

10 minute tutorials

Explore and learn with easy to follow tutorials for multiple use cases
build-icon

Start building in the console

Build your production solution quickly and easily once you're ready

Start Building on AWS Today

Whether you're looking for compute power, database storage, content delivery, or other functionality, AWS has the services to help you build sophisticated applications with increased flexibility, scalability, and reliability.
View AWS Free Tier Details »
Up-Arrow