This course provides a solid foundation in MLOps, helping Data Scientists, Data Analysts, and AI Engineers develop skills in deploying, managing, and optimizing Machine Learning systems in production environments. Learners will explore Linux, Bash scripting, Web APIs with FastAPI, software testing with Pytest, and containerization techniques using Docker and Kubernetes. The course also covers CI/CD with Jenkins, cloud deployment on GCP, Infrastructure as Code with Terraform and Ansible, as well as system monitoring with Prometheus, Grafana, and the ELK stack. Learners will apply their knowledge by deploying an OCR application from development to operation, reinforcing practical MLOps skills.

This course provides a strong foundation in Data Engineering within MLOps, enabling Data Scientists, AI Engineers, and DevOps Engineers to understand how to build and manage data systems for Machine Learning. Learners will gain insights into data architecture concepts such as Data Lake, Data Warehouse, Data Fabric, and Data Mesh. The course also covers batch data ingestion with Apache Spark and streaming data ingestion with Apache Flink, using Kafka and Debezium for real-time data processing. Additionally, learners will work on deploying data pipelines with Apache Airflow, managing a Feature Store with Feast, and building a Lakehouse architecture using Delta Lake, Trino, Hive, and MinIO.

This course offers in-depth knowledge of deploying and optimizing Machine Learning systems in real-world environments. Learners will study high-performance Python optimization, software architecture, training pipeline design, and model deployment strategies. Topics include inference deployment models such as serverless inference using KServe, Model Mesh, and Ray Serve. The course also focuses on using Kubeflow for managing ML pipelines, designing MLOps systems following the C4 model, and implementing real-time monitoring for AI applications.

This course provides advanced knowledge of MLOps system architecture, including software design patterns, data transformation with dbt, data validation with Great Expectations and Deequ, and metadata management using DataHub. Learners will practice distributed training on Kubernetes, manage databases with ORM, deploy an API Gateway with NGINX, and optimize communication with gRPC. Additionally, the course includes LLMOps, covering vLLM, Milvus, and LangChain for managing large language models.