AWS Innovate: Build AI-Enabled Applications and Train Models

This free, online event is for aspiring machine learning developers, applications developers, and data scientists to dive deep into how the cloud can help you build artificial intelligence (AI) applications and machine learning (ML) models. Presented by Amazon AI/ML experts, this event has something for developers and data scientists of all skill levels:

Start your AI/ML journey
Learn ML fundamentals and how to get started building a model with some of the same material Amazon uses to train its own engineers.

Smart applications, no ML experience required
See how you can add intelligence to existing applications with managed AI services that provide vision, speech, language, chatbot, forecasting, and personalization capabilities.

Build, train, and deploy models faster and easier
Experienced ML practitioners will learn how AWS can lower costs and improve the ML workflow from labeling data, to building and training models, and to deploying and hosting models in production.


Date: Tuesday, March 5, 2019
Time: 9:00am–1:30pm Pacific Time
          12:00pm–4:30pm Eastern Time
Location: Online – Register to gain your unique login code

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Event Agenda

Duration
Session Titles
30 mins
30 mins
Keynote
Speaker: Julien Simon
Amazon.com has a long history of using Machine Learning (ML) to solve hard problems at very large scale. Based on this experience, AWS has built a comprehensive stack of ML services that let all organizations add predictive capabilities to their products and services, no matter what their level of expertise is. This is one of the reasons why more ML runs on AWS than anywhere else! In this session, we’ll give you an overview of the AWS ML services, including the new ones launched at AWS re:Invent 2018), highlighting how AWS customers use them to solve real-life problems.

Track 1

60 mins

An Introduction to Machine Learning (100 Level)

Speaker: Blaine Sundrud
This session introduces you to machine learning (ML) by walking you through key phases of the typical ML pipeline, such as problem framing, data cleaning, data visualization and analysis, feature engineering, and model training and evaluation. We will ground the key concepts and processes introduced to you throughout the pipeline in a use case that took place right here at Amazon. Throughout the session, you will also be introduced to different types of ML problems and the various categories of ML algorithms available to you. By the end of this session, our goal is to provide you with a conceptual understanding of the phases of the ML pipeline and an increased familiarity with related key terms and definitions.



10 mins

Break

60 mins

Get Started with Amazon SageMaker: Build, Train, and Deploy Machine Learning Models in the Cloud (200 Level)

Speaker: Denis Batalov
Many organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took considerable time and effort, and it required expertise. In this session, we dive deep into Amazon SageMaker, a fully managed service from AWS that enables developers and data scientists to build, train, and deploy ML models quickly and easily, and at scale. We will discuss the features and benefits of Amazon SageMaker to get your ML models from concept to production.



10 mins

Break

60 mins

Use AWS DeepRacer to Get Rolling with Machine Learning (200 Level)

Speaker: Todd Escalona
Developers, start your engines! This breakout session will provide an introduction to the newly launched AWS DeepRacer. Learn about the basics of reinforcement learning, what’s under the hood and opportunities to get hands on with AWS DeepRacer and participate in the AWS DeepRacer League.






Track 2

Use Natural Language and Text Analysis to Power More Intelligent Applications (200 Level)

Speaker: Sireesha Muppala
AWS brings natural language and text analysis technologies within the reach of every developer through pre-trained AI services. Learn how to modernize, adding intelligence to any application with machine learning services that provide language and chatbot functions. See how others are defining and building the next generation of apps that can interact with the world around us.


Break

Power New Application Experiences with Image, Video, and Facial Analysis (200 Level)

Speaker: Liam Morrison
Computer vision technology enables digital media professionals to generate valuable insights at a quick pace and at a lower cost by automatically identifying the contents of images and video. Integrate Amazon Rekognition into your applications quickly and learn about common use cases such as creating a searchable image library and content moderation.




Break

Integrate Amazon.com’s ML Techniques for Customer Recommendations and Forecasting Workflows into Your Applications (200 Level)

Speaker: Christopher King
Deploying custom machine learning models doesn’t have to be hard based on the machine learning technology perfected from years of use on Amazon.com, Amazon Forecast. and Amazon Personalize. We enable developers with no prior machine learning experience to easily build accurate forecasting and sophisticated personalization capabilities into applications. We'll discuss the features, benefits, and use cases of Amazon Forecast and Amazon Personalize.





Track 3

Get Started with Amazon SageMaker: Build, Train, and Deploy Machine Learning Models in the Cloud (200 Level)

Speaker: Shyam Srinivasan
Many organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took considerable time and effort, and it required expertise. In this session, we dive deep into Amazon SageMaker, a fully managed service from AWS that enables developers and data scientists to build, train, and deploy ML models quickly and easily, and at scale. We will discuss the features and benefits of Amazon SageMaker to get your ML models from concept to production.

Break

Remove Your Biggest Challenge to Getting Started with ML: Using Amazon SageMaker Ground Truth to Label Data at 70% Less Cost (200 Level)

Speaker: Emily Webber
Successful machine learning models are built using high-quality training datasets. Labeling raw data to get accurate training datasets involves time and effort because sophisticated models can require thousands of labeled examples to learn from before they can produce good results. Hear how Amazon SageMaker Ground Truth will help you build highly accurate training datasets for machine learning quickly. Amazon SageMaker Ground Truth offers easy access to public and private human labelers and provides them with built-in workflows and interfaces for common labeling tasks. Additionally, Ground Truth can lower your labeling costs by up to 70% using automatic labeling. This is achieved by training Ground Truth from data labeled by humans so that the service learns to label data independently over time, leading to highly accurate training datasets.



Break

Learn to Build What's Next with Reinforcement Learning on Amazon SageMaker (200 Level)

Speaker: Sunil Mallya
Reinforcement Learning (RL) is an exciting new area within machine learning that enables development of many intelligent applications such as robotics, autonomous vehicles, energy management, financial portfolio management, and many more. With RL, machine learning models can achieve specific outcomes without the need for pre-labeled training data. Dive deep into Amazon SageMaker RL, which takes a different approach to training machine learning models. We will start with the basics of RL and proceed with modeling a simulation environment to represent real-world problems. We will train RL models in this environment and tune them to obtain the required results. The session will end by showcasing AWS DeepRacer as a practical and fun application using RL models.




30 mins
30 mins
ConclusionSpeaker: Julien Simon

Speakers

  • Julien SimonGlobal Evangelist, AI & Machine Learning, AWS

    As the Global Evangelist for Artificial Intelligence & Machine Learning, Julien focuses on helping developers and enterprises bring their ideas to life. He frequently speaks at conferences and he's also actively blogging at https://medium.com/@julsimon. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in top-tier web startups where he led large Software and Ops teams in charge of 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. Last but not least, Julien holds eight AWS certifications.

  • Blaine SundrudInstructional Designer, AWS

  • Denis BatalovTech Leader, AI & ML, AWS

    As a 13-year Amazon veteran and a PhD in Machine Learning, Denis worked on such exciting projects as Search Inside the Book, Amazon Mobile apps and Kindle Direct Publishing. Since 2013 he has helped AWS customers adopt ML & AI technology as a Solutions Architect. Currently, Denis is a Worldwide Tech Leader for ML & AI responsible for the functioning of AWS ML Specialist Solutions Architects globally. Denis is a frequent public speaker, you can follow him on Twitter: @dbatalov.

  • Todd EscalonaSolutions Architect Evangelist, AWS

    As a Solutions Architect Evangelist, Todd spends his time working directly with his customers and partners on a global basis, while listening to understand their goals and working backwards from there. He defines requirements, provides architectural guidance around specific use cases, and assists in designing applications and services that are scalable, reliable, and performant. Outside of speaking at public events and hosting hackathons, Todd’s interests spread across various technologies such as Artificial Intelligence, Machine Learning and serverless event driven architectures.

  • Sireesha MuppalaSolutions Architect, AWS

  • Liam MorrisonPrincipal Solutions Architect, AWS

  • Christopher KingPartner Solutions Architect, AWS

  • Shyam SrinivasanSenior Product Marketing, AI & ML, AWS

    Shyam Srinivasan is a Senior Product Marketing Manager in the AWS AI/ML team and leads the strategy for driving awareness towards educating ML developers and data scientists, focusing on Amazon SageMaker. Shyam constantly engages with customers about machine learning and loves to bring ideas to life with machine learning. Outside of work, Shyam has fun with his family with travel and games.

  • Emily WebberML Specialist, Solutions Architect, AWS

    Emily Webber has been leading data science projects for many years, piloting the application of machine learning into such diverse areas as social media violence detection, economic policy evaluation, computer vision, reinforcement learning, IOT, drone, and robotic design. Her master’s degree is from the University of Chicago, where she developed new applications of machine learning for public policy research with the Data Science for Social Good Fellowship. As a Machine Learning Specialist for Amazon Web Services she guides customers from project ideation to full deployment.

  • Sunil MallyaSenior AI Solutions Architect, AWS

    Sunil Mallya is a lead on the Machine Learning Solutions Lab focused on Deep Learning and Reinforcement Learning at AWS. Sunil is working with AWS customers in various transformation and innovation initiatives across verticals by building models for cutting edge ML/DL/RL apps. Prior to joining AWS, Sunil co-founded the neuroscience and machine learning-based image analysis and video thumbnail recommendation company Neon Labs. He has worked on building large scale low latency systems at Zynga and has an acute passion for serverless computing. He holds a Master’s Degree in Computer Science from Brown University.

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Session Details

Frequently Asked Questions

    • Q: Where is this event?
      This event is an online event, hosted by AWS on the INXPO platform.
    • Q: Who should attend this event?
      This event is ideal for developers, data scientists and all types of builders interested in learning how to build intelligent applications with pre-trained API services. Recommended AI/ML proficiency: 100-200 level.
    • Q: How much does this event cost?
      There is no cost to attend this event.
    • Q: What do these proficiency levels mean?
      Level 100 (Introductory): sessions will focus on providing an overview of AWS services and features, with the assumption that attendees are new to the topic.

      Level 200 (Intermediate): sessions will focus on providing best practices, details of service features and demos with the assumption that attendees have introductory knowledge of the topics.
    • Q: What are the prerequisites before attending the event?
      We recommend that you arrive with an active AWS account to follow test alongside instructor led sessions. We also suggest that you activate free / paid tiers for these services: Amazon Rekognition, Amazon Textract, Amazon Polly, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, and Amazon SageMaker.