Pragmatic AI Labs
4 weeks to complete at 5 hours a week
Build production-grade data and AI systems on the Databricks platform, mastering lakehouse architecture, Delta Live Tables, MLflow, generative AI, and MLOps.
Basic Python programming and familiarity with SQL. No prior Databricks or Spark experience required.
This specialization covers the following topics across five courses: Course 1 - Databricks Lakehouse Fundamentals: - Writing PySpark and SparkSQL queries - Scheduling end-to-end data pipelines with Databricks Jobs - Building and querying Delta Lake tables Course 2 - Data Engineering with Delta Lake on Databricks: - Declarative ETL with Delta Live Tables - Implementing the Medallion Architecture (bronze, silver, gold) - Change Data Capture and incremental processing Course 3 - Machine Learning with Databricks and MLflow: - Reproducible ML experiments with MLflow Tracking - Model versioning and governance with the MLflow Model Registry - Feature Store architecture and production deployment Course 4 - Generative AI and LLMs on Databricks: - Prompt engineering for LLM control and output - Hybrid RAG pipelines with embeddings and vector search - Model security and governance workflows Course 5 - Production Governance and MLOps on Databricks: - Unity Catalog data governance and access control - Continuous Integration and Deployment for ML models - Monitoring, A/B testing, and production model serving
Pragmatic AI Labs is a leading provider of AI and data engineering education, with a mission to make cutting-edge technologies accessible to learners around the world. Their team of expert instructors has deep industry experience building production AI and data systems, and they are committed to delivering hands-on, practical courses that prepare students for real-world success.
This specialization will equip you with the skills to architect and build production-grade data and AI pipelines on the Databricks platform. You'll be able to implement end-to-end data engineering workflows, train and deploy machine learning models with MLflow, and build generative AI applications leveraging large language models and vector search. These skills are in high demand across industries as organizations seek to harness the power of data and AI to drive business outcomes.
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