Track 1-ML Fundamentals

9:50 AM-10:20 AM PDT
Get Started with Machine Learning. No PhD required! (100 Level)

Speaker: Sally Revell
Take your skills and career to the next level by getting started with machine learning (ML) with AWS. Whether you are looking to learn ML hands-on, up-level your professional skill set with online courses or connect with other AWS developers, AWS has a path and pace that works for you. In this session you'll learn about how you can get started with Machine Learning on AWS with services like AWS DeepRacer, AWS DeepLens and AWS Training and Certification. We'll show you how to access learning resources, like the ML courses used to train engineers at Amazon, which are available for free. Last, but not least, you'll learn how you can connect and participate in the AWS community of machine learning developers.

10:20 AM-11:20 AM PDT
An Introduction to Machine Learning (100 Level)

Speaker: Blaine Sundrud
In this session, you build a conceptual framework for understanding machine learning.

We will begin by discussing “what is machine learning” - illustrating some ML use cases and describing the types of ML (supervised vs. unsupervised vs. reinforcement) and ML classification problems (binary, multi-class and regression).

We will then walk through the typical machine learning process, including: 1. Problem Framing, 2. Data Analysis, 3. Model Building, and, 4. Application.

11:20 AM-12:20 PM PDT
Get Started with Amazon Sagemaker: Build, Train, and Deploy Machine Learning Models in the Cloud (200 Level)

Speaker: Shyam Srinivasan
Machine learning (ML) offers innovation for every business. But until recently, developing ML models needed time, effort, and expertise, making it difficult for developers to get started. In this session, we discuss and dive deep into Amazon SageMaker, a fully-managed, modular service that enables developers and data scientists to build, train, and deploy ML models at scale, that helps overcomes those challenges. Join us in this session to learn about Amazon Sagemaker’s capabilities, including data labeling, model building, model training, tuning, and hosting in production.

12:20 PM-1:20 PM PDT
Build deep learning model with computer vision and DeepLens (200 Level)

Speaker: Alex Schultz, AWS ML Hero
Getting started with Machine Learning (ML) may seem difficult for developers. In this chalk talk, you will learn how a developer with no previous ML experience used AWS DeepLens to build a deep-learning-enabled application that can read books to kids.

Track 2-No ML Experience Required

9:50 AM-10:50 AM PDT
Create Smart and Interactive Apps with Intelligent Language Services on AWS (200 Level)

Speaker: Sireesha Muppala
Amazon brings natural language processing, automatic speech recognition, text-to-speech services, and neural machine translation technologies within reach of every developer. In this session, learn how to add intelligence to applications like media metadata analysis, subtitling, and localization with machine learning services that provide language transcription and translation. Watch a live demo on intelligent speech analytics in a customer call center and learn how others are defining and building the next generation of apps that can hear, speak, understand, and interact with the world around us.

10:50 AM-11:20 AM PDT
Future-Proof Your Career: From Java Developer to Machine Learning Practitioner by Kesha Williams, AWS ML Hero (200 Level)

Speaker: Kesha Williams
In this session, you will learn how to level up your skills and career through the journey of Kesha Williams, an AWS ML Hero. Kesha transformed her engineering skills from Java Developer to ML Practitioner by leaning hands-on with AWS AI solutions like AWS DeepLens, Amazon Rekognition, and Amazon SageMaker, as well as investing her 2017 spring break into learning about Tensorflow, Jupyter, and Python. As an ML Practitioner, she integrates technology into everyday life by developing ML models using computer vision on Amazon SageMaker to help us better connect with people around us. Hear the challenges she faced on her journey and how she overcame them using AWS AI solutions.

Break

11:30 AM-11:55 AM PDT
Build a Hotel Recommender Using Amazon Personalize – no PhD required by Pavlos Mitsoulis, AWS ML Hero (200 Level)

Speaker: Pavlos Mitsoulis
Recommender systems are everywhere! Many successful e-commerce companies leverage personalized recommendations to help their customers discover new products, find the right product when they need it, and to discover similar products. The list can be endless. In the industry of travel, you want to inspire travelers with new hotels or find similar hotels based on their preferences. You don't need to be a ML expert to build such a recommendation engine. In this session, you will learn how to easily build and deploy a model using state-of-the-art techniques with Amazon Personalize.

11:55 AM-12:25 PM PDT
Build more accurate forecasting models with machine learning (200 Level)

Speaker: Anuj Gupta
Analyzing and forecasting time series data with traditional methods is a complex and time consuming process that often struggles to produce accurate results for large sets of irregular data by failing to combine it with other relevant independent variables. In this session, we will explore how to accelerate this process by relying on deep learning with Amazon Forecast.

12:25 PM-1:25 PM PDT
Power New Application Experiences with Image, Video, and Facial Analysis (200 Level)

Speaker: Chris Kuthan
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. In this session, we’ll show you how to integrate Amazon Rekognition into your applications quickly and learn about common use cases, such as creating a searchable image library and content moderation.

Track 3-Build, Train, and Deploy ML Models

9:50 AM-10:50 AM PDT
Advanced Machine Learning with Amazon SageMaker (300 Level)

Speaker: Denis Batalov
Amazon SageMaker is a modular, fully managed machine learning (ML) service that enables data scientists and developers to quickly build, train, and deploy ML models into production at scale. In this session, we will dive deep into Amazon SageMaker and how you can deploy your models to production easily and at low cost. We will discuss how popular deep learning frameworks such as TensorFlow, Apache MXNet, and PyTorch are optimized for high performance with SageMaker and how you can leverage these frameworks for your use cases, with an interactive demo.

Break

11:00 AM-12:00 PM PDT
Theoretical Machine Learning: Upcoming Methods, Techniques, and Ideas (400 Level)

Speaker: Emily Webber
Meta-learning, evolutionary search, decomposition, neural architecture search and more! As the demands and challenges machine learning practitioners encounter grow, so too must the methods themselves. Learn how to utilize advanced features within Amazon SageMaker as the backbone for cutting-edge innovation, saving up to 40% of your time and reinvesting that into your own R&D.

Break

12:10 PM-1:10 PM PDT
Making reinforcement learning practical for real-world developers (300 Level)

Speaker: Praveen Veerath
Building machine learning-enabled products are hard for developers and data scientists; throw in a hardware component, and the complexity increases exponentially. Sunil Mallya walks you through how to build complex ML-enabled products using RL, explores hardware design challenges and trade-offs, and details real-life examples of how any developer can up level their RL skills through autonomous driving.

What you'll learn
Learn how to build complex ML-enabled products using RL and about hardwire design challenges and trade-offs
Hear real-life examples of how to level up your RL skills through autonomous driving