Machine Learning and its Needed Enterprise Framework to Survive

Machine Learning is a core component under the umbrella of Artificial Intelligence, a much publicized and popular discussion amongst executives in today’s board rooms. As a result, today’s market place is demanding technology leaders, A.I. mavens, engineers, data scientists, and the business to set forth programs to leverage Machine Learning and various Cognitive Services. What they forget sometimes is, to harness the true power of Machine Learning the right framework of people, process, and technology must be in place and this requires an immense amount of human capital, financial capital, intellectual capital, and more.

To start, we must begin with a business problem. A formal process that starts with business objectives, design, iterative build and train. Second, we all know a framework with the right balance of data science and engineering is crucial to ensuring any advanced modeling is fueled by the right data at the right time. Therefore data must be captured, ingested, aggregated, and compartmentalized and we need to employ armies of engineers, analysts, architects, ETL experts, and data scientists to do it. Third, since Machine Learning (ML) algorithms can be applied to many facets of an enterprises data, start in the area where you can collect feedback from the outcomes, this process is necessary to continually tune and optimize the models themselves to ensure precision and accuracy over time.

So you’ve got the data, you’ve tuned the model, now what? Fourth, you need to re-engineer your DevOps strategy to include optimizing the ML models as a core function; to make sure scale is possible vertically and horizontally as machine learning permeates the enterprise and “model version and release” becomes as common as “software version and release”.