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Course Outline
Foundations of Secure Local AI
- Understanding the meaning of local and on-premises AI in regulated settings.
- Comparing cloud AI with internal deployment strategies for sensitive workloads.
- Exploring common enterprise use cases for private assistants and workflow support.
- Identifying core components of a secure local AI architecture.
Ollama and Open Model Basics
- Understanding 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
- Preparing hosts, workstations, and servers.
- Installing and configuring Ollama for local inference.
- Leveraging containers and internal development tooling.
- Verifying API access and basic operational readiness.
Working with Local Models Effectively
- Executing prompts and shaping outputs using system instructions.
- Utilizing 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 makes a workflow agentic within controlled settings.
- Exploring 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
- Understanding retrieval-augmented generation basics 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
- Constructing a private assistant using Ollama and an open model.
- Implementing retrieval over approved internal documents.
- Incorporating 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 ensuring stakeholder alignment.
- Defining a roadmap for secure local AI adoption.
Requirements
- Foundational understanding of AI concepts and software development principles.
- Familiarity with command-line tools, containerization, 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
