What happens when the world pushes for productionized use-cases around ML? For some companies ML DataOps is baked into their core competencies. For many more, they realize that DataOps is a major component of the overall strategy, but not one they can take on themselves.
That's where we come in. For the thousands of companies we've worked with between high growth and Fortune 50, iMerit improves the quality and reliability of gold standard / training data while minimizing the time needed to realize value from this data. We work at the edge of ML DataOps where automation in addition to human-in-the-loop expertise is needed.
I've been recruited to develop a comprehensive product vision following our $20 million Series-B raise.
Today, I lead 3 major customer-facing initiatives to automate how our customers can achieve their production Artificial Intelligence & Machine Learning goals using nearly any tool required to get the job done (3rd party, client internal and/or iMerit's proprietary annotation & labeling tooling). In addition to leading the design and strategy behind these products, I mentor our product team on how to think about customer-centric problem solving and how to measure success in ways that matter to our strategic goals and to our customer's strategic goals.