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Course Outline
Foundations of Secure Local AI
- The meaning of local and on-premises AI in regulated environments
- Cloud AI versus internal deployment strategies 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
- How Ollama integrates into 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-Premises Environment
- Preparation of hosts, workstations, and servers
- Installing and configuring Ollama for local inference
- Utilizing containers and internal development tooling
- Verifying API access and basic operational readiness
Working with Local Models Effectively
- Running prompts and shaping outputs using system instructions
- Reusing templates for consistent enterprise tasks
- Managing model versions and internal artifacts
- Basic performance tuning for CPU and GPU deployments
Building Practical Agentic Workflows
- Characteristics that define an agentic workflow in a controlled setting
- 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
- Data handling boundaries and privacy considerations
- Access control, logging, and audit support
- Prompt safety, output controls, and guardrails
- 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 with 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 achieving stakeholder alignment
- Defining a roadmap for secure local AI adoption
Requirements
- Foundational understanding of AI concepts and software development
- Familiarity with command-line tools, containers, or local development environments
- Basic scripting or programming experience
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-premises AI adoption
21 Hours
