MLOps Understanding MLOps Basics of MLOps History of MLOps MLOps Essential for ML Pipelines Breaking Down the ML Lifecycle MLOps and DevOps: Differences Role of MLOps in the business MLOps Tools Overview of MLOps Tools Types of MLOps Tools MLOps Tools Grasping Tool Applications Choosing the right MLOps Tools Mastering tool implementation MLOps Environment Setup Preparing local environment Cloud setup for MLOps Grasping Virtual Environments Studying Docker and Kubernetes Setting up CI/CD pipelines Security and permissions Grasping Machine Learning Basics of Machine Learning ML Algorithm Types Feature engineering & selection Model Selection and Training Data preprocessing in ML Validation & Metrics Merging ML with MLOps Grasping the integration process Checking Model compatibility Model versioning A/B Testing and canary releases Learning rollback approaches Monitoring and logging CI/CD in MLOps Learning CI/CD systems in MLOps Exploring CI/CD in MLOps Version control systems in MLOps Testing in MLOps Deploying using CI/CD pipelines Monitoring deployment Data versioning in MLOps Valuing data versioning Data versioning tools (e.g., DVC) Data versioning in projects Data versioning best practices Strategies for large datasets Dealing with sensitive data MLOps practices MLOps workflow overview Customizing MLOps workflows Frequent mistakes and fixes Review MLOps standards ML model life cycle management Case Studies Enhancing MLOps efficiency Boosting MLOps efficiency tips Customizing MLOps Processes Scaling MLOps Organization-wide Enhancing Reusability in MLOps Resource Management Insights Cost Optimization Strategies People & Culture in MLOps Role of Team Culture Defining Team Roles Best Practices for Team Structure Communication in MLOps Teams Ensuring Transparency in MLOps MLOps Management Perspective MLOps Tracking & Logging Monitoring & Logging Basics Impact of Observation in MLOps Exploring Monitoring Tools Setting Up Monitoring & Alerts Log Review for MLOps Feedback in MLOps workflow Future of MLOps Studying new MLOps trends Evaluation of current trends AI and ML effects on MLOps MLOps success stories Predictions for the future of MLOps Insights from leaders in MLOps

For detailed explanations and theory, visit the Complete MLOps.