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
Managing the Complete Machine Learning Lifecycle with MLflow
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.
To solve for these challenges, Databricks unveiled last June MLflow, an open source project that aims at simplifying the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
In this tutorial, we will show you how using MLflow can help you:
Keep track of experiments runs and results across frameworks.
Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker.
What you will learn:
Understand the 3 main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
How to use MLflow Tracking to record and query experiments: code, data, config, and results.
How to use MLflow Projects packaging format to reproduce runs on any platform.
How to use MLflow Models general format to send models to diverse deployment tools.
A fully-charged laptop (8-16GB memory) with Chrome or Firefox
Python 3 and pip pre-installed
Pre-register for a Databricks Standard Trial at http://databricks.com/try
Basic knowledge of Python programming language.
Basic understanding of machine learning concepts.
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.