Machine learning shows tremendous potential for increasing process efficiency. But generating real, lasting value requires more than just the best algorithms. To streamline the AI development process, AI operations have emerged as an important theme. It involves pertinent questions like: how do you maintain an up to date data store? How would you update the model features? how do you keep the code running reliably in production? How do you monitor its performance? How do you deploy new versions of the code to run in parallel with the previous version? The software industry has spent over a decade perfecting DevOps best practices for normal software. We are just getting started with AIOps. A lot of companies have recently launched open sourced platforms to advance the area, Netflix launched meta-flow, Google launched Model Search and commercial investment will be needed in this area as enterprise companies apply AI more broadly or take advantage of its most advanced forms. AIOps companies will be involved in industrializing data gathering, offering model monitoring and performance tools to accelerate time to market and facilitate cost reduction.

The essential pillars of AI Operations include data preparation tools, model development platforms, and performance monitoring. 

Data preparation tools

Data-centric AI is emerging as an innovation theme. Research shows data quality makes a larger impact than model optimization encouraging automation of data labeling and supplying synthetic datasets to supplement human-labeled data. T meet these demands, we anticipate vertically integrated data preparation platforms.  

Model development platforms, and performance monitoring

Model Performance and monitoring tools can bridge the gap between the data science community and IT operations. There is a rising need for explainability, observability, and responsibility in Model Performance & Monitoring tools

In this series, I will share hard lessons for product managers in AI/ML who are thinking about investments in AI Operations!