MLOps is a new discipline that originated from deploying machine learning models to production. Building machine learning models has found its place with the optimal tools, libraries, and frameworks, but, MLOps is still not mature enough to effectively manage models in production. What defines the scope for an end to end MLOps solution varies with the level of Machine Learning adoption. 

MLOps Level 1 – Machine Learning models not yet in production

Any enterprise that wants to convert its data and domain expertise with machine learning models either hire a Data Scientist or look for a customizable solution for their specific use case. Often businesses underestimate the infrastructure needed to build machine learning models. 

Building Machine Learning models involve repetitive experimentation with different algorithms improving accuracy with hyper-parameter tuning. Versioning the experiments are crucial to track and share your work until you find the best fit model to be deployed to production. 

Any Data Science team should be able to be onboarded within minutes by automatically spinning up notebook servers on pre-built infrastructure with automatic logging visualizing dataset statistics.

MLOps Level 2 – Limited number of models in production

Enterprises with a limited number of Machine Learning models deploy all their models to a single server. But single server deployment cannot scale as the data and model grows. One can very well replicate the single server deployment to 2 or more servers but soon manually managing the computing resource needed for serving the models becomes a nightmare. Containerized deployment with Kubernetes has reduced the deployment time exponentially by not only containerizing the codebase but also the libraries and os needed for running the machine learning models. 

But there is a learning curve to learn and deploy models using Kubernetes. What enterprises need at this stage is an automatic deployment solution wherein models can be imported in any format either from GitHub or from any cloud repository. Turn models into API with reusable deployment templates like A/B testing along with automatic scaling of computing resources.

MLOps Level 3 – Increase in Machine Learning models as the business grows

As the business grows, so does the data and the number of models in production. Monitoring models manually are no longer sustainable. Unified monitoring solution tracking both model performance and computing resource becomes crucial. It is not only important to monitor model performance for its accuracy but also for bias, compliance, and feedback loop enhancing diversity and inclusion.

MLOps Level 4 – Beyond Monitoring with Explainable AI

The prediction of machine learning model changes with different datasets used during model training. It is the responsibility of any enterprise employing machine learning to let the customer know the reason why a model behaved in a certain way. For example, it is crucial to explain to the customers why a loan is rejected. Explainable AI is revolutionizing the industry by turning prediction into understanding. AI is no longer a BlackBox with Explainable AI.

Conclusion

There is a need for seamless MLOps solution starting from data engineering to visualizing business insights. For a starter, end to end MLOps solution would mean deploying Machine Learning models with automatic CI/CD pipelines. But for enterprises already managing MLOps deployment would require monitoring capability measuring and alerting on model performance, drift, bias, etc., MLOps is catching up to the growing needs of Machine Learning to effectively manage end to end ML projects.

Our experience working for wide range of customers from healthcare, fintech to retail have led us to invent AIQ solution for managing machine learning projects with confidence.

To Learn more about our AIQ tool:

AIQ Workbench

AIQ Deploy

AIQ Monitor

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