Data Engineering and Data Automation

Scale your ML solutions in the cloud. Use Azure Machine Learning Studio, SDK, AutoML, and Azure Pipelines to deploy and monitor models, mastering pipeline automation, Kubernetes security, and API management.

This course covers advanced topics related to deploying professional machine learning projects on SageMaker. Students will learn how to maximize output while decreasing costs. They will also learn how to deploy projects that can handle high traffic, how to work with especially large datasets, and how to approach security in machine learning AWS applications.

Master Large Language Models (LLMs) and build sophisticated text generation applications in this hands-on course. You’ll master prompt engineering techniques, optimize model selection and costs, and dive deep into Retrieval-Augmented Generation (RAG), using vector databases to ground AI responses in external data and eliminate hallucinations. Finally, you’ll evaluate system performance with RAGAS and showcase your skills by building an end-to-end RAG application.

Become an expert in cloud data engineering with this Nanodegree. Design scalable data architectures, build pipelines, and use Azure tools like Databricks and Synapse to drive powerful data insights.

This advanced Nanodegree prepares you to tackle data science at scale. Work with AI best practices, build production-ready projects, and gain the confidence to lead data-driven solutions.

Refine your machine learning skills with this AWS Machine Learning Engineer Nanodegree. Learn to deploy models on SageMaker and design automated workflows with AWS Lambda and Step Functions.

Design scalable data systems from the ground up. Practice data staging, transformation, and warehouse construction while learning to create centralized architectures for enterprise-wide insights and decision-making.

Guided by an industry expert, this course teaches you how to build scalable data infrastructure, design efficient data pipelines, and develop strategies to support evolving business needs.

Discover the frameworks and architectures behind modern big data systems. This course emphasizes NoSQL and data lake design, culminating in a project where you design an enterprise data lake solution.

Master the fundamentals of data modeling across relational, document, and graph databases. Learn how databases store, query, and enforce structure, and understand the design considerations for OLTP vs. OLAP workloads. Design normalized schemas in PostgreSQL to prevent CRUD anomalies, then model flexible document collections in MongoDB using embedding and referencing strategies. Build graph models in Neo4j with Cypher to represent and traverse connected data. Compare ACID guarantees across all three paradigms, explore managed cloud services on AWS, and apply your skills by designing a complete multi-database backend for a growing e-commerce company.

Master Apache Airflow to build, schedule, and monitor data pipelines on AWS. Start with Airflow fundamentals—DAGs, tasks, XCom, Jinja templating, branching, and runtime configuration—then apply production patterns like single-responsibility design, data intervals, asset-driven scheduling, and dynamic task mapping. Build complete ETL and ELT pipelines that move data through S3 into Amazon Redshift using SQL operators, template inheritance, and data constraint checks. Then construct a modern data lakehouse using S3, Glue, Iceberg, and Athena, automating ingestion and promotion through bronze, silver, and gold layers while handling schema evolution. Deploy pipelines to Amazon MWAA and apply monitoring and observability best practices for production environments.

Explore the key components of modern data governance. Learn to implement metadata systems, assess data quality, and manage master data to support accurate decision-making and business intelligence.