Make the Most of Your Talent and Time When Working on AI and ML Projects – Automate the Rest

Tired of spending too much time on manually intensive tasks to onboard and prepare data for your AI and ML projects? A proliferation of loosely integrated point tools and the lack of automation results in a great deal of time spent writing glue code and coordinating tooling, instead of training and operationalizing your ML models. There is a better way, and it starts with automation. In this session we’ll discuss how you can automate the manually intensive and time-consuming work of onboarding and preparing your data; so you can focus your talent and time on making the best use of AI and ML to further your business goals.


 
Try Databricks
« back
About Ramesh Menon

Infoworks

Prior to Infoworks, Ramesh led the team at Yarcdata that built the world’s largest shared-memory appliance for real-time data discovery, and one of the industry’s first Spark-optimized platforms. At Informatica, Ramesh was responsible for the go-to-market strategy for Informatica’s MDM and Identity Resolution products. Ramesh has over 20 years of experience building enterprise analytics and data management products.

About Kevin Holder

Infoworks

Kevin Holder manages the customer solutions architecture and engineering team at Infoworks, specializing in data engineering for big data and cloud data. Prior to Infoworks, Kevin held leadership roles at Couchbase and Informatica, where he was responsible for architecting customer solutions employing NoSQL and data warehouse technology. Working with hundreds of companies over 20 years, Kevin has deep knowledge of how to scope, architect and deliver successful technical projects.