In every company, thousands of AI models will be required to automate and enhance workflows and accelerate the innovation of new digital products. Existing machine learning systems take months to develop and deploy a single model. Reducing the time that it takes to develop accurate, production-ready models is critical to solving a large number of business challenges with AI.
In this workshop, we’ll cover how Driverless AI automates some of the most difficult data science and machine learning workflows such as feature engineering, model validation, model tuning, model selection, and model deployment. You’ll learn how to deploy production-ready models as an AWS Lambda function or using Amazon Sagemaker.
Please finalize your registration here: https://www.eventbrite.com/e/accelerate-machine-learning-deployment-with-h2o-driverless-ai-on-aws-tickets-61206491263
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Join this half-day workshop for a hands-on experience of Driverless AI on AWS. In this session you will learn more about:
How to ingest and visualize your dataset
Feature engineering to obtain the most accurate results from algorithms
Robust interpretability of machine learning models to explain modeling results
Scoring pipelines and new ultra-low latency automatic scoring pipelines
Deploying models as an Amazon Lambda Function or using Amazon Sagemaker platform
Agenda at a glance
8:30-9:00am Registration, Breakfast & Networking
9:00-9:30am Introduction to H2O Driverless AI
9:30-10:00am Customer Stories and Use Cases
10:00-10:45am Interactive Demo: Automatic feature engineering
10:45-11:30am Interactive Demo: Machine Learning Interpretability
11:30-12:00pm Interactive Demo: Deploying scoring pipelines using AWS Lambda and Sagemaker
Space is limited for this event. Sign up today to reserve your spot!
Speaker: Pratap Ramamurthy, Senior Principal Solution Architect, H2O.ai