How to Create a Successful AI Solution: Start With Data
Forrester predicts that 2018 will be the year when a majority of enterprises start dealing with the hard facts: AI and all other new technologies like big data still require hard work. AI is on the rise, with enterprises beginning to understand how the technology can help them improve efficiency and productivity.
Still, there’s a widening gap between the future-looking rhetoric dominating the space and pragmatic and tangible recommendations. How do you determine if AI is right for your business? And - perhaps the bigger question - how do you get started building an AI powered solution? The short answer: the success of AI depends on data. It’s critical that early adopters serious about integrating AI in their businesses consider the best practices around tagging and training data for AI systems.
Our Co-Founder and COO Heather Ames sat down with Director of Product Rakesh Patel and CloudFactory Go-To-Market Strategist Matthew McMullen to discuss the biggest problems product teams encounter when building an AI data plan.
- What we've learned from tagging over 1 million images for enterprise businesses
- How to maintain control of your data and keep it private
- How we've saved Fortune 500 companies time and money tagging and training data
- Best practices around data quality, quantity and new state-of-the-art methods for data tagging