As we mentioned, making a business problem statement is crucial when it comes to building machine learning applications. However, since it is not techy and exciting a lot of people de-prioritize it and overlook it. So, the advice is – spend some time on your problem, think about it and think about what are you trying to achieve. Don't just look at it from the perspective of "I want more clicks on my website", or "I want to earn more money". A well-defined problem looks something like this – "What helps me sell more e-books?".

As we mentioned, making a business problem statement is crucial when it comes to building machine learning applications. However, since it is not techy and exciting a lot of people de-prioritize it and overlook it. So, the advice is – spend some time on your problem, think about it and think about what are you trying to achieve. Don't just look it from the perspective "I want more clicks on my website", or "I want to earn more money". A well-defined problem looks something like this – "What helps me sell more e-books?".

We recently published a new whitepaper, Machine Learning Best Practices in Financial Services, that outlines security and model governance considerations for financial institutions building machine learning (ML) workflows. The whitepaper discusses common security and compliance considerations and aims to accompany a hands-on demo and workshop that walks you through an end-to-end example. Although the whitepaper focuses on financial services considerations, much of the information around authentication and access management, data and model security, and ML operationalization (MLOps) best practices may be applicable to other regulated industries, such as healthcare. A typical ML workflow, as shown in the following diagram, involves multiple stakeholders. To successfully govern and operationalize this workflow, you should collaborate across multiple teams, including business stakeholders, sysops administrators, data engineers, and software and devops engineers.

Netflix's famous algorithm challenge awarded a million dollars to the best algorithm for predicting user ratings for films. But did you know that the winning algorithm was never implemented into a functional model? Netflix reported that the results of the algorithm just didn't seem to justify the engineering effort needed to bring them to a production environment. At your company, you can create the most elegant machine learning model anyone has ever seen. That's no easy feat, which is why we're presenting you with seven machine learning best practices.

Netflix's famous algorithm challenge awarded a million dollars to the best algorithm for predicting user ratings for films. But did you know that the winning algorithm was never implemented into a functional model? Netflix reported that the results of the algorithm just didn't seem to justify the engineering effort needed to bring them to a production environment. At your company, you can create the most elegant machine learning model anyone has ever seen. That's no easy feat, which is why we're presenting you with seven machine learning best practices.