Fundamentals of MLOps

Step into the dynamic world of Machine Learning Operations (MLOps) with our tailored course specifically designed for DevOps engineers eager to expand their skill sets and embrace the intersection of machine learning and operational excellence. This course provides a robust introduction to MLOps, covering crucial concepts, methodologies, and tools that merge the spheres of data science and DevOps.

1. Introduction to MLOps:

•  Understand the core principles of MLOps and its necessity in the modern tech landscape.
•  Explore the evolving role of an MLOps engineer in a data-driven world.
•  Differentiate MLOps from DevOps by examining the collaboration of DataOps, ModelOps, and DevOps.
•  Navigate the MLOps lifecycle, focusing on CI/CD, continuous training, and monitoring strategies.
•  Analyze high-level MLOps architecture and its components.

2. Data Collection and Preparation:

•  Master the intricacies of data collection, ingestion, and the concept of data lakes.
•  Gain hands-on experience in data cleaning and transformation using Pandas, Polars, and large scale tools like Apache Spark and Dask.
•  Dive into the world of streaming data with Apache Kafka and Apache Flink.
•  Discover the role of feature stores and learn to orchestrate data pipelines using Airflow and Prefect.

3. Model Development and Training:

•  Acquire skills in model development and training, including hyperparameter tuning techniques.
•  Understand computing landscapes, emphasizing the use of CPUs and GPUs for efficient model training.
•  Get introduced to MLflow for experiment management and model lifecycle through detailed demos and labs.

4. Model Deployment and Serving:

•  Investigate model deployment and serving with tools like BentoML, addressing model drift and version upgrades.
•  Explore the use of monitoring tools such as Prometheus, Grafana, and Evidently to ensure continuous model performance.

5. Automating Insurance Claim Reviews with MLflow and BentoML:

•  Apply your MLOps knowledge to a practical project by deploying an application for automating insurance claim reviews.
•  Learn to set up MLflow servers and integrate BentoML for seamless model serving within a Python Flask application.

6. Data Security and Governance:

•  Explore critical aspects of data privacy, security, and access management.
•  Navigate compliance landscapes, focusing on GDPR, HIPAA, and PCI standards and their implications.

Learning Outcomes:

By the end of this course, DevOps engineers will have a solid foundation in MLOps, enriched with the skills needed to design, deploy, monitor, and manage machine learning models effectively. This course empowers participants to merge their DevOps expertise with machine learning practices, staying at the forefront of technological innovation.