The Machine Learning Race is upon us. Every organization is seeking to outpace their competition by leveraging AI/ML to drive differentiation for their business. To win this race, companies are building up data science teams, investing in faster/more scalable cloud data platforms and utilizing the growing variety of publicly available algorithms and ML toolkits. Yet, organizations ramping up these initiatives soon find that their ML processes are only useful if the data that is feeding them is clean and structured for the task at hand. They quickly learn that scaling machine learning is entirely dependent upon scaling data wrangling processes.
Join Forrester VP & Principal Analyst, Mike Gualtieri and Trifacta Head of Platform Product Management Mahesh Gandhe for a live webinar covering organizational best practices for scaling data preparation in order to scale ML and AI initiatives.
In this latest Data Science Central webinar you can expect to learn:
Common data prep bottlenecks in machine learning such as data quality, feature engineering & data blending
How data preparation platforms improve scale, collaboration and automation of wrangling data for AI
Organizational best practices and deployment scenarios for data preparation & machine learning in cloud, on-premises and hybrid/multi-cloud environments
Mike Gualtieri, VP & Principal Analyst - Forrester
Mahesh Gandhe, Head of Platform Product Management - Trifacta
Stephanie Glen, Editorial Director - Data Science Central