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mlops
Learn the basics of MLOps in this free tutorial. You will learn version control, docker, Monitoring and Logging for ML, and other such concepts in detail.
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Overview of MLOps and its importance
Common challenges in ML development and deployment
Key principles of MLOps
Introduction to Git and GitHub
Best practices for version controlling ML projects
Branching and merging strategies
Managing data and model artifacts with Git LFS
Introduction to Docker and containerization
Building Docker images for ML applications
Running containers locally and on the cloud
Best practices for containerizing ML applications
Overview of CI/CD pipelines for ML
Setting up a CI/CD pipeline with Jenkins
Automating Testing and Validation of ML Models
Deploying ML models with Kubernetes
Importance of monitoring and logging in MLOps
Setting up monitoring and logging infrastructure
Defining metrics and alerts for ML models
Visualizing and analyzing model performance
Introduction to distributed computing for ML
Setting up a distributed ML environment with Apache Spark
Scaling ML workloads with Kubernetes and Docker Swarm
Best practices for scaling ML workloads
Overview of ethical considerations in MLOps
Bias and fairness in ML models
Privacy and security considerations in MLOps
Implementing ethical guidelines in MLOps