Published: 17 June 2019
Gartner receives hundreds of different machine learning questions each week, from all kinds of clients. Here, we provide answers to those most frequently asked, to help data and analytics leaders understand issues before they arise, frame next steps and guide their planning process.
Included in Full Research
- 1. Definitions and Market Trends
- How Do You Define Machine Learning?
- How Do You Define Artificial Intelligence?
- Is the Current Hype Around ML and AI Justified?
- What Are the Relationships Between ML, AI, Deep Learning, Data Science and Advanced Analytics?
- What Business Problems Are Addressed by ML?
- 2. Business Impacts
- How Do We Evaluate the ROI From AI Techniques (Including ML) Investments?
- Do You Have Guidelines/Best Practices for AI and ML Contracts? How Do We Manage Any Issues Around Intellectual Property of Data?
- How Do We Differentiate With ML?
- How to Operationalize Models at Scale?
- 3. Market Size and Adoption
- What Is the Market Size of ML?
- What Are the Most Common ML Use Cases?
- What Is Driving Further Adoption of ML?
- 4. Technology
- What Are the Technical Pitfalls of ML?
- How Do We Implement ML Solutions?
- What Is Augmented Analytics and How Does It Relate to ML?
- 5. Organization
- How Do We Get Started With ML?
- How Do We Staff for ML?
- What Are the Organizational Challenges of ML?
- How Can We Be Sure the Vendor’s Claims of Deploying AI Are True?
- 6. Infrastructure and Platform
- How Are In-House-Developed ML Models Delivered Into the Organization?
- Do We Need Special Infrastructure for ML?
- Should We Use Cloud or On-Premises Infrastructure for ML?
- 7. Other Information Sources
- What Newsletters Are There to Stay Up to Date?
- What Are the Major Conferences and Summits?
- How Can I Learn More About ML?