What you’ll take away.
- Design a structured RAG or agent architecture on Databricks.
- Govern data, context and access in a traceable way.
- Assess quality, security, cost and operations using suitable tests.
Your training in detail
- Who is it for?
- Data engineers, ML engineers and development teams planning or operating generative AI applications on the Databricks Data Intelligence Platform.
- Prerequisites
- Foundations in Python, SQL and data platforms, plus initial experience with machine-learning or generative AI workflows.
- Format & planning
- Indicative format: 3 to 5 days with a project lab. Scope, platform access and any exam alignment are agreed before training.
Agenda: from understanding to application
Our independently developed agenda proposal. Focus areas, lab environment and scope are agreed before booking.
Bound the use case, data sources and measurable quality criteria
Design context preparation, embeddings and the retrieval pipeline
Develop model and agent workflows and connect tools
Evaluation, security, governance and MLflow tracking
Deployment, monitoring, cost and an improvement cycle
Build a small source-grounded assistant using a synthetic knowledge set. Define test questions, evaluate retrieval and answers, and document operating and governance boundaries.
Exams, licences & credentials
Alignment with the Databricks Certified Generative AI Engineer Associate exam can be agreed. The exam and certification are external services and are included only when explicitly stated in the proposal.
Vendor references & review date
Sources checked on 2026-10-06. These sources describe the technology or external exam requirements. They do not imply a partnership with NextNowa.
Your learning content
These objectives describe the training topic. The specific syllabus, duration, dates, trainer and price are agreed before booking.