Published: 31 July 2017
Gartner receives hundreds of different questions per week on machine learning, from all kinds of clients. This note offers data and analytics leaders concise answers and further drilldown pointers to the most frequently asked questions, and should help guide their planning process.
Included in Full Research
- 1. Definitions and Market Trends
- How Do You Define Machine Learning?
- How Does ML Work?
- How Do You Define Artificial Intelligence?
- Is the Current Hype Around AI and ML 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 ML/AI Investments?
- Do You Have Guidelines/Best Practices for ML/AI Contracts? How Do We Manage Any Issues Around Intellectual Property of Data?
- How Do We Differentiate With ML?
- 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 Claims of Deploying AI Is 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
- What Newsletters Are There to Stay Up-to-Date?
- What Are the Major Conferences and Summits?
- How Can I Learn More About ML?