Coffee Sessions #138

Explainability in the MLOps Cycle

When it comes to Dattaraj's interest, you'll hear about his top 3 areas in Machine Learning. What he sees as up and coming, what he's investing his company's time into and where he invests his own time. Learn more about rule-based systems, deploying rule-based systems , and how to incorporate systems into more systems. there is no difference between ML systems and deploying models. It's just that this machine learning model is much smarter than traditional rule based models.

Take-aways

Transparency @ 3 Stages in MLOps Pipeline - Stage #1 – Algorithmic Choice and Code - Stage #2 – Business Validation - Stage #3 – Inferencing Local vs Global Explanations Counterfactual explanations

In this episode

Dattaraj Rao

Dattaraj Rao

Chief Data Scientist, Persistent

Dattaraj Jagdish Rao is the author of the book “Keras to Kubernetes: The Journey of a Machine Learning Model to Production”. Dattaraj leads the AI Research Lab at Persistent and is responsible for driving thought leadership in AI/ML across the company. He leads a team that explores state-of-the-art algorithms in Knowledge Graphs, NLU, Responsible AI, MLOps and demonstrates applicability in Healthcare, Banking, and Industrial domains. Earlier, he worked at General Electric (GE) for 19 years building Industrial IoT solutions for Predictive Maintenance, Digital Twins, and Machine Vision. Dattaraj held several Technology Leadership roles at Global Research, GE Power, and Transportation (now part of Wabtec). He led the Innovation team out of Bangalore that incubated video track inspection from an idea into a commercial Product. Dattaraj has 11 patents in Machine Learning and Computer Vision areas.

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Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

Vishnu Rachakonda

Vishnu Rachakonda

Host

Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.