The Automation and Optimization of Advanced Analytics based on Machine Learning
TDWI Speaker: Philip Russom, Senior Research Director for Data Management
Date: Thursday, May 31, 2018
Time: 9:00 a.m. PT, 12:00 p.m. ET
A number of trends have converged recently to make machine learning (ML) more desirable and practical than ever:
- User organizations need a wider range of analytics to be competitive, profitable, agile, and innovative.
- Moore’s Law has taken us to a higher level of speed and scale, so we can do bigger and better analytics with big data, social media, and IoT data.
- Analytics tools are better than ever at automating and optimizing ML.
- Data professionals are closing the skills gap by adopting new analytic methods that leverage machine learning
However, embracing machine learning successfully is challenging due to the non-trivial data requirements to deploy ML solutions. During development, creating a functional ML model requires large volumes of diverse data. In production, a deployed ML model still requires voluminous data to learn and improve over time. In turn, managing big data for machine learning demands a robust data management infrastructure and tool portfolio.
In this TDWI webinar, you will learn about:
- The data, tool, platform, and technology requirements for machine learning
- How automation can help optimize ML-driven advanced analytics
- ML’s development environment, production systems, voracious appetite for data, and actionable output
- How to download for free TDWI’s new Checklist Report on the automation and optimization of machine learning
Philip Russom, Ph.D.