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Duration 21 hours
Course Outline
Foundations of Secure On-Premises AI Deployment
- Defining local and on-premises AI capabilities within regulated sectors
- Comparing cloud-based AI solutions against internal deployments for sensitive data workloads
- Key enterprise application scenarios for private assistants and operational workflow support
- Essential architectural components of a secure, localized AI infrastructure
Ollama Framework and Open-Source Model Fundamentals
- The role of Ollama within a localized development ecosystem
- Procedures for retrieving, executing, and managing AI models in local environments
- Criteria for selecting models based on scale, performance, hardware requirements, and licensing terms
- Aligning model capabilities with specific organizational business objectives
Establishing the On-Premises Environment
- Preparation of host systems, workstations, and server infrastructure
- Installation and configuration of Ollama for localized inference tasks
- Implementation of containerization and internal development tools
- Verification of API connectivity and confirmation of operational readiness
Effective Utilization of Local AI Models
- Executing prompts and refining outputs through structured system instructions
- Utilizing reusable templates to ensure consistency across enterprise-level tasks
- Management of model versions and internal digital artifacts
- Optimization strategies for CPU and GPU-based deployments to enhance performance
Developing Practical Agentic Workflows
- Characteristics that define agentic workflows in controlled, secure settings
- Basic design patterns for planning, tool integration, and iterative response cycles
- Creating task-oriented assistants to support internal operations
- Incorporating human oversight, fallback mechanisms, and robust error handling
Private Retrieval-Augmented Workflows
- Foundations of retrieval-augmented generation for accessing internal knowledge bases
- Processes for preparing documents for segmentation, indexing, and search optimization
- Integration of local vector stores with Ollama-based applications
- Techniques for enhancing retrieval precision and response quality
Security, Governance, and Compliance Protocols
- Establishing data handling boundaries and addressing privacy concerns
- Implementing access controls, comprehensive logging, and audit trails
- Ensuring prompt safety, output regulation, and implementation of guardrails
- Governance checkpoints required for deployment and operation in regulated sectors
Enterprise Integration Strategies
- Exposing localized AI capabilities via secure internal APIs
- Integrating AI assistants with existing internal applications and services
- Supporting diverse use cases including assistive, batch, and automated workflow tasks
- Maintaining solution integrity within controlled network perimeters
Evaluation of Local AI Solutions
- Assessing model quality, operational reliability, and output consistency
- Conducting tests against specific business, policy, and safety mandates
- Comparing various model options tailored to distinct enterprise functions
- Establishing a continuous improvement cycle for internal development teams
Practical Implementation Laboratory
- Constructing a private assistant using Ollama and an open-source model
- Incorporating retrieval capabilities over approved internal documentation
- Implementing basic agentic actions with integrated safety controls
- Reviewing key deployment, operational, and governance milestones
Strategic Adoption Planning and Next Steps
- Reviewing critical architectural and deployment decisions
- Identifying common challenges in regulated AI project execution
- Planning pilot initiatives and ensuring stakeholder alignment
- Defining a strategic roadmap for secure local AI adoption
Requirements
- Fundamental understanding of AI concepts and software development practices
- Proficiency with command-line interfaces, containers, or local development environments
- Basic scripting or programming experience
Target Audience
- Developers and technical teams constructing private AI solutions on internal infrastructure
- Security, compliance, and platform professionals supporting AI initiatives in regulated sectors
- Technical leaders in finance, healthcare, government, and defense sectors evaluating on-premises AI adoption
