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 enterprises 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 machine learning, 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 for the second part 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 in the model-building process.
In Part 2, you will learn about:
March 2 - Mistakes to Avoid When Building and Deploying Machine Learning Programs: Part 1
May 6 - Mistakes to Avoid When Building and Deploying Machine Learning Programs: Part 2
July 8 - Mistakes to Avoid When Building and Deploying Machine Learning Programs: Part 3
Date: May 6, 2021
Time: 9:00AM PT
Individual, Student, and Team memberships available.