AI/ML Bootcamp is a 2 day event for Machine Learning (ML) aspiring developers, application developers, ML developers and data scientists that want to learn and apply ML at speed and scale. Presented by Amazon AI/ML experts, this series of sessions and hands-on workshops has something for developers and data scientists of all machine learning skill levels.
On Day 1, you learn how to get hands on with machine learning using AWS DeepRacer and how to add computer vision, language, recommendation, and forecasting intelligence with pre-trained AI services. On Day 2, you learn how to build, train, and deploy ML models at scale with ML Services that cover the entire ML workflow: label and prepare your data, choose an algorithm, train the model, tune and optimize it for deployment, make predictions, and take action.
This event is perfect for beginners that want to dive deep into AI/ML, as well as advanced ML practitioners who want to build, train, and deploy models at massive scale.
Must have an active AWS account and introductory knowledge of AWS. Please bring your laptop to participate in workshops.
We also suggest that you activate these services:
- Amazon Comprehend
- Amazon Rekognition
- Amazon SageMaker
- Amazon Polly
- Amazon Transcribe
- Amazon Translate
Amazon.com has a long history of using Machine Learning (ML) to solve hard problems at a 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.
Level: 100
AWS brings natural language and text analysis technologies within reach of every developer through pre-trained AI services. Learn how to modernize by 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.
Level: 200
Integrate Amazon.com’s ML Techniques for Customer Recommendations and Forecasting Workflows Into Your Applications
Deploying custom machine learning models don'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.
Level: 200
Machine learning is being increasingly used to improve customer engagement by powering personalized product and content recommendations, tailored search results, and targeted marketing promotions. However, developing the machine learning capabilities necessary to produce these sophisticated recommendation systems has been beyond the reach of most organizations today due to the complexity of developing machine learning functionality. Amazon Personalize allows developers with no prior machine learning experience to easily build sophisticated personalization capabilities into their applications, using machine learning technology perfected from years of use on Amazon.com.
Level: 200
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, effort, and 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.
Level: 200
Remove Your Biggest Challenge to Getting Started with ML: Using Amazon SageMaker Ground Truth to Label Data at 70% Less Cost
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.
Level: 200
Come and help build the most accurate text classification model possible. A fully managed machine learning (ML) platform, Amazon SageMaker enables developers and data scientists to build, train, and deploy ML models using built-in or custom algorithms. In this workshop, you learn how to leverage Keras/TensorFlow deep learning frameworks to build a text classification solution using custom algorithms on Amazon SageMaker. You package custom training code in a Docker container, test it locally, and then use Amazon SageMaker to train a deep learning model. You then try to iteratively improve the model to achieve a higher level of accuracy. Finally, you deploy the model in production so different applications within the company can start leveraging this ML classification service.
Level: 300