Secure Local Agentic AI: On-Prem Ollama Development for Regulated Industries Training Course

Foundations of Secure Local AI

• Understanding the concept of local and on-premises AI within regulated settings.
• Comparing cloud-based AI with internal deployment 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 Fundamentals

• 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 ensuring basic operational readiness.

Working Effectively with Local Models

• Executing prompts and shaping outputs using system instructions.
• Leveraging templates for consistent enterprise tasks.
• Managing model versions and internal artifacts.
• Basic performance tuning for CPU and GPU deployments.

Building Practical Agentic Workflows

• What defines an agentic workflow within 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 accessing internal knowledge.
• 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.
• Ensuring solutions remain 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 using 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 aligning stakeholders.
• Defining a roadmap for secure local AI adoption.