Ollama Training in Gaia

This live, instructor-led training in Gaia empowers finance professionals and IT staff with the ability to implement Ollama locally. Attendees will learn to deploy, configure, and integrate large language models into secure financial workflows, while applying best practices for compliance and automation.

This live training in Gaia empowers healthcare practitioners and IT teams to deploy Ollama for local LLMs. Participants will learn to integrate AI into clinical workflows, tailor models for specific operational tasks, and uphold rigorous data privacy and regulatory compliance standards within secure healthcare environments.

This instructor-led training in Gaia explores responsible AI principles within Ollama, with a specific focus on fairness, transparency, and accountability. Participants will design governance workflows, implement bias mitigation techniques, and establish compliance monitoring frameworks through hands-on labs and real-world case studies.

This instructor-led, live training in Gaia provides intermediate professionals with the expertise to securely deploy Ollama. Gain the ability to apply differential privacy, establish secure logging mechanisms, and enforce data access controls that align with regulatory compliance requirements for resilient on-premises environments.

This advanced, instructor-led training on Gaia delves into the diagnosis, testing, and evaluation of local Ollama deployments. It encompasses systematic debugging, robust evaluation pipelines, observability, and automated CI/CD integration to ensure production reliability and operational readiness.

This instructor-led, live training in Gaia (online or onsite) is designed for advanced professionals seeking to fine-tune and customize AI models on Ollama for enhanced performance and domain-specific applications.

Upon completion of this training, participants will be able to:

• Establish an efficient environment for fine-tuning AI models on Ollama.
• Prepare datasets for supervised fine-tuning and reinforcement learning.
• Optimize AI models for improved performance, accuracy, and efficiency.
• Deploy customized models within production environments.
• Evaluate model enhancements and ensure system robustness.

This instructor-led, live training in Gaia (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.

This instructor-led, live training in Gaia (online or onsite) is aimed at intermediate-level professionals who wish to deploy, optimize, and integrate LLMs using Ollama.

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

• Set up and deploy LLMs using Ollama.
• Optimize AI models for performance and efficiency.
• Leverage GPU acceleration for improved inference speeds.
• Integrate Ollama into workflows and applications.
• Monitor and maintain AI model performance over time.

This instructor-led, live training in Gaia (online or in-person) targets beginner-level professionals who want to install, configure, and utilise Ollama to run AI models on their own machines.

Upon completion of this training, participants will be able to:

• Grasp the fundamentals of Ollama and its capabilities.
• Configure Ollama for running local AI models.
• Deploy and interact with LLMs using Ollama.
• Optimise performance and resource utilisation for AI workloads.
• Investigate use cases for local AI deployment across various industries.

Ollama is an open-source tool for running large language models locally on consumer and enterprise hardware. It abstracts model quantization, GPU allocation, and API serving into a single command-line interface, enabling organizations to self-host LLMs like Llama, Mistral, and Qwen without sending prompts or data to OpenAI, Anthropic, or Google.