Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of Secure Local AI
- Understanding the meaning of local and on-premises AI in regulated environments.
- Comparing cloud-based 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.
- Matching model options to practical business tasks.
Preparing the On-Prem Environment
- Preparing hosts, workstations, and servers for deployment.
- Installing and configuring Ollama for local inference.
- Leveraging containers and internal development tooling.
- Verifying API access and ensuring 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 characteristics that make a workflow agentic within a controlled setting.
- Implementing simple patterns for planning, tool use, 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 operations.
- Connecting a local vector store to an Ollama-based application.
- Enhancing relevance and answer quality through improved retrieval patterns.
Security, Governance, and Compliance Practices
- Establishing data handling boundaries and addressing privacy considerations.
- Implementing access control, logging, and audit support mechanisms.
- Ensuring prompt safety, output controls, and guardrails.
- Defining 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.
- 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 ensuring stakeholder alignment.
- Defining a roadmap for secure local AI adoption.
Requirements
- Fundamental understanding of AI concepts and software development principles.
- Familiarity with command-line tools, containers, 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 the adoption of on-premises AI.
21 Hours
