Kuber Deokar

Kuber is responsible for the coordination of online courses and ensures seamless interactions between the management teams, course creators, course instructors, teaching assistants, and students. Kuber also handles continuous course improvement projects in his capacity as Data Science Lead at UpThink Edutech Services. He has a special interest in Machine Learning, Predictive Analytics, Statistical Modeling, SQL, R, and app development.

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Machine Learning Operations 1 (MLOps1-GCP): Deploying AI & ML Models in Production using Google Cloud Platform (GCP) (edX)

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we [...]

Machine Learning Operations 1 (MLOps1-AML): Deploying AI & ML Models in Production using Microsoft Azure Machine Learning (AML) (edX)

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we [...]

Machine Learning Operations 1 (MLOps1-AWS): Deploying AI & ML Models in Production using Amazon Web Services (AWS) (edX)

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we [...]

Machine Learning Operations 2 (MLOps2-AML): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning (AML) (edX)

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course, MLOps2: Data [...]

Machine Learning Operations 2 (MLOps2-GCP): Data Pipeline Automation & Optimization using Google Cloud Platform (GCP) (edX)

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course, MLOp2s: Data [...]

Machine Learning Operations 2 (MLOps2-AWS): Data Pipeline Automation & Optimization using Amazon Web Services (AWS) (edX)

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course - MLOp2s: [...]