Databricks is a very popular environment for developing data science solutions. More and more companies are interested in Databricks as it is very simple to set up and contains a collaborative workspace for working with a team of people. In this session you will see how to create Machine Learning Solutions with multiple workflows starting with ETL, to data exploration, model experimentation, and lastly to a production release of a data science solution.

Today, more and more development is performed on very large datasets. Attendees will learn how to use Apache Spark, which is part of Databricks, to rapidly analyze lots of data. Learn how to use Databricks to reduces operational complexity to create solutions which can be scaled up or down depending on the amount of data needed to process without having to change the underlying code.

Python, Jupyter Notebooks, and Apache Spark are the technologies used to create solutions within this session. No experience is required.

Agenda

• Introduction to Databricks environment

• Hadoop and Apache Spark Basics

• Jupyter Notebooks and Python Foundations

• Machine Learning Data Requirements

• Data extraction and Understanding with Spark

• Creating Machine Learning Models

• Evaluating Machine Learning Models

• Production Machine Learning Pipelines in Databricks

• Machine Learning Data Processing with Databricks