Published: 14 September 2018
Machine learning continues to gain traction in digital businesses, and technical professionals must embrace it as a tool for creating operational efficiencies. This updated primer discusses the benefits and pitfalls of machine learning, architecture updates, and new roles and responsibilities.
Included in Full Research
- What Is Machine Learning?
- What Business Trends and Benefits Are Driving Machine Learning?
- Examples of How Machine Learning Can Deliver Value to Organizations
- Machine Learning Can Also Provide Process Benefits for IT Organizations
- Business Strengths and Challenges of Machine Learning
- Driving Business Value by Integrating Machine Learning and BPM
- How Should IT Prepare for Machine Learning?
- Learn the Stages of the Machine Learning Process
- Understand the Model Development Life Cycle Needed for Machine Learning
- Understand the Basic Architecture Needed for Machine Learning
- Develop a Framework for Designing ML Workflows
- Applying Machine Learning to Machine Learning
- Leverage Machine Learning as a Service (MLaaS) to Accelerate Delivery
- A Comprehensive End-to-End Architecture
- Understand What Skills Will Be Needed for Machine Learning
- Steps to Get Started With Machine Learning
- Understand How Business Needs Intensify Based on Maturity of ML Environment
- Learn About and Experiment With ML Concepts and Technology
- Work Closely With Data Science Teams and Business Users to Identify a Use Case
- Build a Use Case in the Cloud
- Iteratively Expand Your ML Platform and Services Over Time