Did you know that most AI projects never get fully deployed? In fact, a recent survey by NewVantage Partners revealed that only 15% of leading enterprises have gotten any AI into production at all. Unfortunately, many models get built and trained, but never make it to business scenarios where they can provide insights and value. This gap – deemed the production gap – leaves models unable to be used, wastes resources and stops AI ROI in its tracks. But it’s not the technology that is holding things back. In most cases, the barriers to businesses and organizations becoming data-driven can be reduced to three things: people, process and culture. So, the question is, how can we overcome these challenges and start getting real value from AI? To overcome this production gap and finally get ROI from their AI, enterprises must consider formalizing an MLOps strategy.
MLOps, or machine learning operations, refers to the culmination of people, processes, practices and underpinning technologies that automate the deployment, monitoring and management of machine learning models into production in a scalable, fully governed way. Read More
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