Avoid These Data Pitfalls When Moving Machine Learning Applications Into Production

How often have you heard “The Machine Learning Application worked well in the lab, but it failed in the field. “? It is not the fault of the Machine Learning Model!

Image 1. Failure. Source: Unsplash by cuttersnap

Warning!

This blog is not yet another blog article (YABA) on DataOps, DevOps, MLOps, or CloudOps.

I do not mean to imply xOps is not essential.

For example, MLOps is both strategic and tactical. It promises to transform the “ad-hoc” delivery of Machine Learning applications into software engineering best practices.

A guide to MLOps for data scientists

What are the Symptoms of the Problems of Deploying Machine Learning Applications?

We know the symptoms: Most machine-learning models trained in the lab perform poorly on real-world data [1, 2, 3, 4].

Why 90 percent of all machine learning models never make it into production

Why is it So Hard to Integrate Machine Learning into Real Business Applications?

What is the critical Problem with Machine Learning Success?

Machine Learning created profits in the year 2020 and will continue to increase profits in the future. However, many problems hold back the progress and success of Machine Learning application rollout to production.

I focus on what it is the most significant problem or cause: the quality and quantity of input data in Machine Learning models [1,4].

We realized the quantity of high-quality data was the bottleneck in predictive accuracy when we started…