Watson Assistant Hands-on Lab
The Watson Assistant lab/workshop will walk you through the steps needed to create a virtual agent using Watson Assistant Lite on IBM Cloud.
Prerequisite: Attendees will need to sign up for a free IBM Cloud account prior to the conference.
Introduction to Driverless AI v1.7.0
In this hands-on training, we will introduce you to automated feature engineering, model building, visualization, and interpretability. Additionally, we will showcase automatic report generation and one-click model deployment, and highlight new features of the 1.7.0 release. We will conduct this hands-on training using H2O.ai's training platform.
Prerequisite: Attendees will need to sign up for a free H2O account prior to the conference.
Hands on with Ascend Autonomous Dataflow Service
Participants will compete to solve complex data challenges by building continuously-optimized, Spark-based data pipelines against real-world datasets. Using the Ascend Autonomous Dataflow Service, participants will be able to:
Build large-scale pipelines using declarative configurations and 85% less code, to get up and running in a matter of minutes.
Handle unexpected data changes from upstream systems and APIs, to decrease pipeline maintenance.
Collaborate and iterate within their team by tapping into live data feeds to fuel downstream analytics and machine learning on-demand.
Prerequisite: Attendees must be comfortable working in SQL
Enabling the AI-Driven Enterprise with Automated Machine Learning
Learn how DataRobot’s industry-leading automated Machine Learning platform addresses the predictive model building and deployment needs for a broad spectrum of users - from advanced Data Scientists to beginner Data Analysts and everyone in between. In this session, Gourab De, VP of Data Science, will focus on a Customer Lifetime Value use case to demonstrate the workflow from model training and tuning all the way to deployment.
No laptops needed, but please bring your curiosity and questions to make the session very interactive!
Building Data Pipelines for Apache Spark™ with Delta Lake
Delta is Databricks’ next-gen engine built on top of Apache Spark. This course is for data engineers, architects, data scientists and software engineers who want to use Databricks Delta for building pipelines for data lakes with high data reliability and performance. The course will cover typical data reliability and performance challenges that data lakes face and teach how to address them using Delta. The course ends with a capstone project building a complete data pipeline using Databricks Delta. Topics Covered Include:
Creating Delta tables for a data lake
Appending records to a Databricks Delta table
Performing UPSERTs of data into existing Databricks Delta tables
Reading and writing streaming data into a data lake
Optimizing a data pipeline and optimization best practices
Comparison with Lambda architecture
Getting streaming Wikipedia data into a data lake via Kafka broker
Writing streaming data into a raw table
Cleaning up bronze data and generating normalized query tables
Creating summary tables of key business metrics
Creating plots/dashboards of business metrics
Completed the Getting Started with Apache Spark™ SQL, Getting Started with Apache Spark™ DataFrames, or ETL Part 1 course, or already have similar knowledge.
Tour of the Comcast Technology Center
This guided tour showcases the unique Comcast urban campus and educates employees, business partners, and community partners about Comcast’s history, corporate culture, and products.