Explore the essentials of MLOps with a practical, hands-on approach to machine learning operations. Starting with the fundamentals of the MLOps lifecycle, the course introduces key concepts like continuous integration, continuous deployment, continuous training, and continuous monitoring. With a focus on tools such as MLflow, BentoML, Apache Kafka, and AWS SageMaker, you'll learn to build scalable and efficient machine learning pipelines. Data preparation is covered in-depth, from ingestion to transformation using Pandas, Polars, and Dask. You'll also work with Apache Kafka for streaming data and explore how to orchestrate data pipelines using tools like Apache Airflow. The course dives into model development and training, using MLflow for tracking experiments, managing model versions, and storing artifacts. In the model deployment and serving section, you'll see how to manage model drift and ensure reliable serving using BentoML. Key real-world applications, such as automating insurance claims reviews, will guide you through the full deployment cycle. Additionally, you'll understand the significance of data security and compliance in MLOps, including GDPR, HIPAA, and PCI. Lastly, the course provides a sneak peek into AWS SageMaker, a cloud platform for MLOps.