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Course Outline
Foundations of Secure Local AI
- Understanding local and on-premise AI within regulated environments.
- Comparing cloud AI with internal deployment for sensitive workloads.
- Common enterprise use cases for private assistants and workflow support.
- Core components of a secure local AI architecture.
Ollama and Open Model Basics
- The role of Ollama in a local development stack.
- Pulling, running, and managing models locally.
- Selecting models based on size, quality, hardware requirements, and licensing.
- Aligning model options with practical business tasks.
Preparing the On-Premise Environment
- Preparing hosts, workstations, and servers.
- Installing and configuring Ollama for local inference.
- Utilizing containers and internal development tooling.
- Verifying API access and establishing basic operational readiness.
Working with Local Models Effectively
- Executing prompts and shaping outputs using system instructions.
- Reusing templates for consistent enterprise tasks.
- Managing model versions and internal artifacts.
- Performing basic performance tuning for CPU and GPU deployments.
Building Practical Agentic Workflows
- Defining what constitutes an agentic workflow in a controlled setting.
- Implementing simple patterns for planning, tool usage, and response loops.
- Designing task-focused assistants for internal operations.
- Incorporating human review, fallback logic, and error handling.
Private Retrieval Workflows
- Basics of retrieval-augmented generation for internal knowledge access.
- Preparing documents for chunking, indexing, and search.
- Connecting a local vector store to an Ollama-based application.
- Enhancing relevance and answer quality through improved retrieval patterns.
Security, Governance, and Compliance Practices
- Establishing data handling boundaries and addressing privacy considerations.
- Implementing access control, logging, and audit support.
- Ensuring prompt safety, output controls, and guardrails.
- Defining governance checkpoints for regulated deployment and operation.
Enterprise Integration Patterns
- Exposing local AI capabilities through internal APIs.
- Integrating assistants with internal applications and services.
- Supporting assistant, batch, and workflow automation use cases.
- Maintaining solutions within controlled network boundaries.
Evaluating Local AI Solutions
- Assessing quality, reliability, and consistency.
- Testing against business, policy, and safety requirements.
- Comparing model options for specific enterprise tasks.
- Establishing a practical improvement cycle for internal teams.
Hands-On Implementation Lab
- Building a private assistant using Ollama and an open model.
- Adding retrieval capabilities over approved internal documents.
- Introducing simple agentic actions and safety controls.
- Reviewing deployment, operations, and governance checkpoints.
Adoption Planning and Next Steps
- Reviewing key design and deployment decisions.
- Identifying common pitfalls in regulated AI projects.
- Planning pilot use cases and aligning stakeholders.
- Defining a roadmap for secure local AI adoption.
Requirements
- Fundamental understanding of AI concepts and software development.
- Familiarity with command-line tools, containerization, or local development environments.
- Basic experience in scripting or programming.
Audience
- Developers and technical teams constructing private AI solutions on internal infrastructure.
- Security, compliance, and platform professionals supporting AI initiatives in regulated environments.
- Technical leaders in finance, healthcare, government, and defense sectors evaluating on-premise AI adoption.
21 Hours
