Unified Analytics | Half-Day Workshop

Unifying Data Pipelines and Machine Learning with Apache Spark. ™

EVENT DETAILS
August 28, 2019
Microsoft San Francisco
555 California street, suite 200
San Francisco, CA  94104 

Every enterprise today wants to accelerate innovation by building AI into their business. However, most companies struggle with preparing large datasets for analytics, managing the proliferation of ML frameworks, and moving models in development to production.

Please join Attunix, Microsoft, and Databricks for this half-day immersive experience into Databricks. We’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources.  

You’ll also learn how to use ML frameworks (i.e. TensorflowXGBoostScikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production.

Register for this half-day workshop to learn how unified analytics can bring data science and engineering together to accelerate your ML efforts. This free workshop will give you the opportunity to: 

  • Learn how to build highly scalable and reliable pipelines for analytics 
  • Deeper insight into Apache Spark and Azure Databricks, including the latest updates with Databricks Delta  
  • Train a model against data and learn best practices for working with ML frameworks (i.e. – XGBoostScikit-Learn, etc.) 
  • Learn about MLflow to track experiments, share projects and deploy models in the cloud and on-prem 
  • Network and learn from your ML and Apache Spark peers 

 

AGENDA AT A GLANCE

8:30-9:00 – Registration, Breakfast & Networking  

9:00-9:45 – Opening Remarks – Unifying Data Science and Data Engineering  

9:45-10:15 – Customer Use Case  

10:15-10:45 – Networking with Peers 

10:45-11:30 – Data Engineering Interactive Demo & Best Practices: Preparing Data for Analytics 

11:30-12:15 – Data Science Interactive Demo & Best Practices: Model Training and Machine Learning 

12:15-12:30 – Q&A  

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