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Course Outline
Principles of Secure Local Artificial Intelligence
- Definitions of local and on-premises artificial intelligence within regulated contexts
- Comparison of cloud-based versus internal deployment for sensitive data processing
- Typical enterprise applications for private assistants and workflow assistance
- Essential elements of a secure local AI infrastructure
Fundamentals of Ollama and Open Models
- The role of Ollama within local development environments
- Procedures for downloading, executing, and administering models locally
- Selecting appropriate models based on capacity, accuracy, hardware constraints, and licensing terms
- Aligning model capabilities with specific operational requirements
Establishing the On-Premises Environment
- Preparation of host systems, workstations, and servers
- Installation and configuration of Ollama for local inference
- Leveraging containers and internal development utilities
- Confirming API connectivity and initial operational status
Optimizing Use of Local Models
- Executing prompts and directing outputs through system directives
- Utilizing standardized templates for uniform enterprise tasks
- Administrating model versions and internal repositories
- Fundamental performance optimization for CPU and GPU architectures
Developing Practical Agentic Workflows
- Characteristics of agentic workflows within controlled settings
- Fundamental patterns for planning, tool utilization, and response cycles
- Designing task-oriented assistants for internal operations
- Incorporating human oversight, fallback mechanisms, and error management
Private Retrieval Workflows
- Retrieval-augmented generation fundamentals for accessing internal knowledge bases
- Processing documents for chunking, indexing, and search functionality
- Linking local vector stores to Ollama-based applications
- Enhancing relevance and response quality through improved retrieval strategies
Security, Governance, and Compliance Standards
- Data handling limitations and privacy protections
- Access management, logging requirements, and audit capabilities
- Prompt safety measures, output controls, and guardrails
- Governance milestones for regulated deployment and operation, including considerations for government use cases
Enterprise Integration Approaches
- Exposing local AI capabilities via internal APIs
- Integrating assistants with existing internal applications and services
- Supporting assistant-based, batch processing, and workflow automation initiatives
- Maintaining solutions within controlled network perimeters
Assessment of Local AI Solutions
- Evaluating quality, reliability, and consistency standards
- Testing against business objectives, policy mandates, and safety requirements
- Comparing model options for specific enterprise applications
- Establishing a continuous improvement cycle for internal teams
Implementation Laboratory
- Constructing a private assistant using Ollama and an open-source model
- Implementing retrieval capabilities over approved internal documentation
- Incorporating basic agentic functions and safety controls
- Reviewing deployment, operational, and governance checkpoints
Adoption Strategy and Future Directions
- Analysis of key design and deployment decisions
- Identification of common challenges in regulated AI projects
- Planning pilot initiatives and securing stakeholder alignment
- Defining a roadmap for the secure adoption of local AI, including opportunities for government implementation
Requirements
- Fundamental knowledge of artificial intelligence principles and software engineering practices
- Working familiarity with command-line interfaces, containerization technologies, and local development setups
- Introductory experience in scripting or programming languages
Audience
- Engineers and technical personnel deploying private AI systems on internal infrastructure for government applications
- Security, compliance, and platform specialists facilitating AI integration within regulated sectors
- Technical leadership in financial services, healthcare, and defense communities assessing on-premises AI adoption strategies
21 Hours
