A Practical Guide to Rapidly Improving AI Products

AI teams are too often dazzled by what they can produce with the many tools and frameworks available to them, but tools and frameworks don't know your business. Success in building AI systems involves the almost-too-obvious strategies outlined in this report: understanding your own data and measuring it by meaningful metrics, examining actual AI-to-customer interactions to identify and correct real issues, and other approaches that are firmly grounded in business realities.

If you're thinking of building an AI product, or if you're already dealing with an underperforming product, this report by consultant Hamel Husain provides clear advice on the most common mistakes that AI teams make, illustrated with examples of his clients' specific challenges and their successful resolution

• Understand why error analysis is essential to top AI performance
• Build a customized interface that's worth the investment
• Empower nontechnical experts with AI skills
• Effectively use synthetic data for testing
• See why AI product roadmaps prioritize experimentation over deadlines