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Course Outline
Foundations of Secure Local AI
- Understanding the implications of local and on-premises AI in regulated environments
- Comparing cloud-based AI with internal deployment for sensitive workloads
- Common enterprise use cases for private assistants and workflow support
- Essential components of a secure local AI architecture
Ollama and Open Model Basics
- The role of Ollama in a local development stack
- Procedures for pulling, running, and managing models locally
- Criteria for selecting models based on size, quality, hardware, and licensing
- Aligning model options with practical business tasks
Preparing the On-Premises Environment
- Preparing hosts, workstations, and servers for local AI deployment
- Installing and configuring Ollama for local inference operations
- Utilizing containers and internal development tooling for streamlined processes
- Verifying API access and initial operational readiness for government applications
Working with Local Models Effectively
- Executing prompts and shaping outputs using system instructions
- Reusing templates to ensure consistency in enterprise tasks
- Managing model versions and internal artifacts for government use
- Basic performance tuning for CPU and GPU deployments in a secure environment
Building Practical Agentic Workflows
- Characteristics of agentic workflows in controlled settings for government operations
- Simple patterns for planning, tool integration, and response loops
- Designing task-focused assistants to enhance internal operations for government
- Incorporating human review, fallback logic, and error handling mechanisms
Private Retrieval Workflows
- Basics of retrieval-augmented generation for secure access to internal knowledge
- Preparing documents for chunking, indexing, and search in a government context
- Connecting a local vector store to an Ollama-based application for government use
- Enhancing relevance and answer quality through optimized retrieval patterns
Security, Governance, and Compliance Practices
- Establishing data handling boundaries and addressing privacy concerns in government operations
- Implementing access control, logging, and audit support for compliance
- Ensuring prompt safety, output controls, and guardrails for secure use
- Setting governance checkpoints for regulated deployment and operation in government environments
Enterprise Integration Patterns
- Exposing local AI capabilities through internal APIs for government systems
- Integrating assistants with internal applications and services for enhanced functionality
- Supporting assistant, batch, and workflow automation use cases in a secure manner
- Ensuring solutions remain within controlled network boundaries for government operations
Evaluating Local AI Solutions
- Assessing quality, reliability, and consistency of local AI solutions for government use
- Testing against business, policy, and safety requirements specific to government operations
- Comparing model options to meet specific enterprise tasks in a government setting
- Establishing a practical improvement cycle for internal teams within government agencies
Hands-On Implementation Lab
- Constructing a private assistant using Ollama and an open model for government applications
- Adding retrieval capabilities over approved internal documents for enhanced security
- Introducing simple agentic actions and safety controls to ensure compliance
- Reviewing deployment, operations, and governance checkpoints for government use
Adoption Planning and Next Steps
- Reviewing key design and deployment decisions for secure local AI in government
- Identifying common pitfalls in regulated AI projects within government agencies
- Planning pilot use cases and aligning with stakeholders for government operations
- Defining a roadmap for the secure adoption of local AI solutions in government
Requirements
- A foundational understanding of artificial intelligence (AI) concepts and software development practices
- Experience with command line tools, containerization technologies, or local development environments
- Basic proficiency in scripting or programming languages
Audience
- Developers and technical teams working on private AI solutions within internal infrastructure
- Security, compliance, and platform professionals ensuring the support of AI in regulated environments
- Technical leaders in sectors such as finance, healthcare, government, and defense who are assessing the adoption of on-premises AI technologies
21 Hours
