MLOps | Virtual Event
machine learning at scale

Successfully building a machine learning model is hard enough. Tracking thousands of experiments, reproducing results at scale, moving models into production, redeploying and rolling out updated models is exponentially harder. To address these challenges, many organizations are building custom “ML platforms” to automate and standardize the end-to-end ML lifecycle.

Watch our talks below to learn more about the latest developments and best practices for building ML platforms, MLOps, and how managing and standardizing the full ML lifecycle on Databricks with MLflow can help organizations solve these common challenges and accelerate innovation.

Featured Speakers

Matei Zaharia
CTO and Co-founder

Keven Wang
Competence Lead, ML Engineer

Wesly Clark
Chief Architect, Enterprise Analytics and AI
J.B. Hunt Transport

Cara Phillips
Data Science, MLOps Consultant
Artis Consulting

Part 1: Opening Keynote

MLOps and ML Platforms State of the Industry — Matei Zaharia, CTO and Co-founder, Databricks and Clemens Mewald, Director of Product Management, Databricks

Watch now | Slides Pt1 | Slides Pt2

Part 2: Product Demo

End-to-end MLOps for PyTorch on Databricks using MLflow — Sean Owen, Principal Architect, Databricks.

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Part 3: Customer Story featuring H&M

Applying MLOps at Scale — Keven Wang, Competence Lead, ML Engineer, H&M – an overview of H&M reference architecture and demo of the production workflow. This talk will cover the entire MLOps stack addressing a few common challenges in AI and Machine learning products, like development efficiency, end to end traceability, speed to production, etc.

Watch now | Slides

Part 4: Customer Story featuring J.B. Hunt Transport and Artis Consulting

CI/CD in MLOps - Implementing a Framework for Self-Service Experimentation and Deployment at Enterprise Scale —Wesly Clark, Chief Architect, Enterprise Analytics and AI, J.B. Hunt Transport and Cara Phillips, Data Science, MLOps Consultant, Artis Consulting – this talk will cover the core values, concepts, and conventions of the framework followed by a technical demo of how to implement the self-service automation of Databricks resources, code, and jobs deployment into Azure DevOps CI/CD pipelines.

Watch now | Slides

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Free Tutorial: Introducing MLflow on Databricks

In this simple hands-on tutorial, we’ll take a look at how health data can be used to predict life expectancy. It will start with data engineering in Apache Spark, data exploration, model tuning and logging with hyperopt and MLflow. It will continue with examples of how the model registry governs model promotion, and simple deployment to production with MLflow as a job or dashboard.

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Recommended Spark + AI Summit Tech Talks

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

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Session 1
Automatic Forecasting using Prophet, Databricks, Delta Lake and MLflow at Atlassian
Session 2
Productionizing Deep Reinforcement Learning with Spark and MLflow at Zynga
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
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