Mistakes to Avoid When Building and Deploying Machine Learning Programs: Part 1
Webinar Speaker: Fern Halper, TDWI VP Research, Senior Research Director for Advanced Analytics
Date: Tuesday, March 2, 2021
Time: 9:00 a.m. PT, 12:00 p.m. ET
Machine learning—where systems examine data to identify patterns with minimal human intervention—is becoming part of the analytics fabric of many organizations as its competitive value becomes understood. It is no surprise, then, that TDWI research indicates that demand for machine learning is growing. In fact, machine learning is currently in the early stages of adoption among TDWI survey respondents. If users stick to their plans, a majority of organizations will be using the technology in the next few years.
Although many companies are excited about machine learning, they often overlook several key success factors. To succeed in ML, embrace the full machine learning life cycle in a unified way—from data management/governance and data engineering to building the model and putting it in production, all while ensuring that your organization’s culture embraces predictive applications.
What does it take to succeed with a machine learning program? Join TDWI’s VP of Research Fern Halper in the first of a three-part series about succeeding with machine learning. Fern will speak with Santiago Giraldo, Cloudera’s Director of Product Marketing, Data Engineering & Machine Learning, about mistakes to avoid and best practices for success when preparing models for machine learning.
In Part 1, you will learn about:
- The machine learning life cycle
- Mistakes to avoid and best practices to follow when preparing for machine learning, including model formulation, data preparation, and modern data pipeline development
- Cultural considerations for succeeding with machine learning
Fern Halper, Ph.D.