Practical ML | Virtual Event
Machine learning for a dynamic world

This virtual event covers real-world examples and demos of how the Databricks Unified Data Analytics Platform can help data teams build the foundation and processes required to respond to business needs faster in a dynamic world. Specifically, we’ll cover the following:

  • How to work around scarce or obsolete data to produce reliable insights, using data augmentation or transfer learning
  • How to enable frictionless transitions from experimentation to production
  • How to maintain historical models, increase training frequency, and accelerate time to market with MLOps and Databricks as your machine learning platform

Clemens Mewald

Director of Product Management Databricks

Sean Owen

Principal Solutions Architect Databricks

Part 1: Opening Keynote

Building a modern data science and Machine Learning platform for real-time response – Opening Keynote by Clemens Mewald, Director of Product Management at Databricks.

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Part 2: Product Demo

End-to-end Data Science and Machine Learning on Databricks with MLflow: Improving Forecasting models during COVID-19 - Product Demo by Sean Owen, Principal Solutions Architect at Databricks.

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Part 3: Customer Story

Scaling machine learning practice and COVID-19 challenges - Fireside Chat with Matt Turner, Chief Data Officer at Medical University of South Carolina and Ben Lorica, Chief Data Scientist at Databricks.

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What's Next?

Join our free training series to learn how Databricks can help you quickly move from experimentation to production-scale machine learning model deployments. Notebooks and data set will be provided so you can follow along and practice at your own pace.

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A collection of data science and machine learning talks from leading industry experts from Atlassian, Zynga, Starbucks, and more.

flèche précédente
Session 1
Automatic Forecasting using Prophet, Databricks, Delta Lake and MLflow at Atlassian
Session 2
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Session 3
Translating Models to Medicine an Example of Managing Visual Communications at Seattle Children's
Session 4
Operationalizing Machine Learning at Scale at Starbucks
Session 5
Generative Hyperloop Design: Managing Massively Scaled Simulations Focused on Quick-Insight Analytics and Demand Modelling
Session 6
Patterns and Anti-Patterns for Memorializing Data Science Project Artifacts at BlueCross BlueShield
Session 7
Saving Energy in Homes with a Unified Approach to Data and AI at Quby
Session 8
Machine Learning Data Lineage with MLflow and Delta Lake
flèche suivante

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