Model Online / On-Site
Duration
Modules 4 modules

Course Objective

To provide hands-on mastery of the three core pillars of the IBM watsonx ecosystem (watsonx.ai, watsonx.data, and watsonx.governance) within a unified architectural framework tailored for hybrid cloud and regulated industries.

Participants gain practical experience combining IBM Granite and open-source foundation models with enterprise data to build trustworthy, auditable, and scalable AI applications.

Target Audience and Prerequisites

  • Target Audience: Enterprise AI architects, data scientists, ML engineers, and technical professionals leading AI initiatives in regulated industries (finance, telecom, public sector).
  • Prerequisites: Basic Python and SQL skills, along with familiarity with machine learning and large language model (LLM) concepts.

Key Takeaways and Focus Areas

  • Building Prompt Lab templates, Tuning Studio jobs, RAG pipelines, and agentic workflows with watsonx.ai studio and SDKs.
  • Managing enterprise data and vector indexes on an open lakehouse architecture (Apache Iceberg, Presto, Milvus) with watsonx.data.
  • Enforcing model risk management, automated factsheet generation, drift monitoring, and EU AI Act compliance via watsonx.governance.

Curriculum

IBM watsonx Architecture and Model Engineering with watsonx.ai
  • watsonx components on hybrid cloud, IBM Granite models, and the open model catalog
  • Adapting models for enterprise tasks using Prompt Lab, Tuning Studio, and the Python SDK
Open Lakehouse and Enterprise RAG Architecture with watsonx.data
  • Configuring Apache Iceberg, Presto query engines, and the integrated Milvus vector database
  • Secure data preparation and semantic search pipelines across distributed enterprise data sources
Compliance, Risk, and Lifecycle Management with watsonx.governance
  • Automating model lineage and documentation with AI Factsheets
  • Monitoring fairness, hallucinations, drift, and regulatory compliance dashboards
Production Deployment, MLOps, and Enterprise Integration
  • Publishing models and prompt templates as services via Deployment Spaces
  • End-to-end integration with Red Hat OpenShift and enterprise application layers

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