Building Private AI Workflows with Ollama

• Experience with AI model deployment and management
• Familiarity with network security and access control
• Understanding of enterprise automation and DevOps practices

Audience

• Enterprise architects designing AI-powered workflows
• Security analysts ensuring compliance and data privacy
• Automation engineers integrating AI into business operations

Ollama enables organizations to build private AI workflows by deploying and running large language models (LLMs) locally, ensuring full control over data security, compliance, and system performance.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to implement secure and efficient AI-driven workflows using Ollama.

By the end of this training, participants will be able to:

• Deploy and configure Ollama for private AI processing.
• Integrate AI models into secure enterprise workflows.
• Optimize AI performance while maintaining data privacy.
• Automate business processes with on-premise AI capabilities.
• Ensure compliance with enterprise security and governance policies.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Private AI with Ollama

• Overview of Ollama’s role in enterprise AI
• Benefits of running AI models privately
• Comparison with cloud-based AI solutions

Setting Up a Secure AI Infrastructure

• Deploying Ollama on on-premise and self-hosted servers
• Configuring access controls and authentication
• Implementing encryption for AI model data

Deploying AI Models in a Private Environment

• Loading and managing LLMs locally
• Optimizing performance for private deployments
• Ensuring AI model version control and updates

Building Secure AI Workflows

• Designing AI-driven automation pipelines
• Integrating Ollama with enterprise applications
• Ensuring compliance with security and governance policies

Optimizing AI Model Performance and Efficiency

• Leveraging GPU acceleration for high-speed processing
• Fine-tuning AI models for private workloads
• Monitoring and maintaining AI performance

Ensuring Compliance and Data Privacy

• Best practices for enterprise AI security
• Data retention policies for private AI models
• Regulatory compliance considerations (GDPR, HIPAA, etc.)

Scaling Private AI Workflows

• Expanding AI capabilities in large enterprises
• Hybrid approaches combining private and cloud AI
• Future trends in private AI deployment

Summary and Next Steps