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Course Outline
Foundations of Secure Local AI
- The significance of local and on-premise AI in regulated industries.
- Comparing cloud-based AI with internal deployment for sensitive workloads.
- Typical enterprise use cases for private assistants and workflow support.
- Key architectural components of a secure local AI system.
Ollama and Open Model Essentials
- The role of Ollama in a local development stack.
- Local management of model pulling, execution, and lifecycle.
- Criteria for selecting models based on size, quality, hardware requirements, and licensing.
- Aligning model capabilities with practical business tasks.
Setting Up the On-Premise Environment
- Preparation of hosts, workstations, and servers.
- Installation and configuration of Ollama for local inference.
- Leveraging containers and internal development tools.
- Validating API access and establishing basic operational readiness.
Efficient Local Model Usage
- Executing prompts and refining outputs through system instructions.
- Utilizing templates for consistent enterprise-level tasks.
- Managing model versions and internal artifacts.
- Basic performance optimization for CPU and GPU deployments.
Constructing Practical Agentic Workflows
- Defining agentic workflows within a controlled setting.
- Simple patterns for planning, tool utilization, and response loops.
- Designing task-specific assistants for internal operations.
- Incorporating human review, fallback logic, and error handling mechanisms.
Private Retrieval Workflows
- Basics of retrieval-augmented generation for internal knowledge access.
- Document preparation for chunking, indexing, and search.
- Connecting local vector stores to Ollama-based applications.
- Enhancing relevance and answer quality through optimized retrieval patterns.
Security, Governance, and Compliance Practices
- Data handling boundaries and privacy considerations.
- Implementing access control, logging, and audit capabilities.
- Managing prompt safety, output controls, and guardrails.
- Establishing governance checkpoints for regulated deployment and operations.
Enterprise Integration Patterns
- Exposing local AI capabilities via internal APIs.
- Integrating assistants with internal applications and services.
- Supporting use cases involving assistants, batch processing, and workflow automation.
- Maintaining solution containment 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
- Developing a private assistant using Ollama and an open-source model.
- Implementing retrieval over approved internal documents.
- Introducing basic 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
- A foundational understanding of AI concepts and software development practices.
- Experience with command-line tools, containerization, or local development environments.
- Basic proficiency in scripting or programming.
Target Audience
- Developers and technical teams engineering private AI solutions on internal infrastructure.
- Security, compliance, and platform specialists supporting AI initiatives in highly regulated settings.
- Technical leaders in finance, healthcare, government, and defense sectors assessing on-premise AI adoption.
21 Hours
