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MLOps

Your team of data scientists are best at handling data and getting the “most” out of it but it is your operations team who utilizes that “most” to generate business value.

What is MLOps?

MLOps is a set of practices that combines Machine Learning, DevOps, and Data Engineering, which aims to deploy and maintain ML systems in production reliably and efficiently.  It applies to the entire Machine Learning Lifecycle, starting from data pipeline to model generation, orchestration, and deployment, to health, diagnostics, governance, and business metrics. Imagine an ML model deployed in production. MLOps takes care of retraining and deployment of the model with new incoming data, manages model versions and related artifacts, and monitors model health in production. It helps to increase automation and improve the quality of production ML.

What is MLOps for a business?

MLOps is a seamless integration of your development cycle and your operations that enables your data scientists to work through the lens of business interest and create better agile ML products.

MLOps Capabilities

The Continuous Delivery Foundation SIG MLOps summarizes MLOps capabilities as follows:
MLOps combines the ML product release cycle and software application release.
The adoption of MLOps best practices enables supporting machine learning models and datasets and makes them first-class citizens within CI/CD systems.
It enables automated testing of machine learning artifacts (e.g. data validation, ML model testing, and ML model integration testing)
MLOps is means to reduce technical debt across machine learning models.
It brings agility to a machine learning project.
To build scalable ML systems, MLOps has become a necessity, and getting it right is key to success for your ML initiatives.

ML Explainability

We understand that knowing the 'why' can help us learn more about the problem, the data, and the reason a model might fail.

Imagine you are working as a credit analyst at a financial institution and are using a smart algorithm developed by your tech team to evaluate the creditworthiness of customers. Undoubtedly, it has improved the efficiency but isn’t this scary that not even the developers of the algorithm understand how exactly it evaluates a customer profile and makes the decision it does — or even worse, how to prevent someone exploiting it.

Well, here comes the critical role of Model Explainability or more precisely Model Interpretability.

Interpretability can be defined as the degree to which a human can understand the cause of a decision or the degree to which a human can consistently predict a model's result. The higher the interpretability of a model, the easier it is to comprehend why certain decisions or predictions have been made.

On certain occasions, it is just the prediction that is of importance, and the “why” part can be ignored. For example, Netflix making a certain movie recommendation. However, there are situations where “why” can be a matter of life and death. Imagine, a self-driving car killing a coachman. Obviously, who could have incorporated horse carts in the model.