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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 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

""Trifacta brought an entirely new level of productivity to the way our analyst and IT teams explore diverse data and define analytic requirements. Our users can intuitively and collaboratively prepare the growing variety of data that makes up PepsiCo’s analytic initiatives.""

""We were actually able to shave the amount of time it took to do the analysis by [a factor of] six. Rather than having to do a tremendous amount of analysis, we’re actually readily able to start getting incremental data products out quickly.""