Ollama Training in Patras

Ollama is a lightweight platform designed for running large language models locally.

This instructor-led, live training (available online or onsite) targets intermediate-level finance practitioners and IT personnel aiming to implement, customize, and operationalize Ollama-based AI solutions within financial environments.

Upon completing this training, participants will acquire the skills necessary to:

• Deploy and configure Ollama for secure use in financial operations.
• Integrate local LLMs into analytical and reporting workflows.
• Adapt models to finance-specific terminology and tasks.
• Apply security, privacy, and compliance best practices.

Format of the Course

• Interactive lecture and discussion.
• Hands-on financial data exercises.
• Live-lab implementation of finance-focused scenarios.

Course Customization Options

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

Ollama is a lightweight platform for running large language models locally.

This instructor-led, live training (online or onsite) is aimed at intermediate-level healthcare practitioners and IT teams who wish to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative environments.

Upon completing this training, participants will be able to:

• Install and configure Ollama for secure use in healthcare settings.
• Integrate local LLMs into clinical workflows and administrative processes.
• Customize models for healthcare-specific terminology and tasks.
• Apply best practices for privacy, security, and regulatory compliance.

Format of the Course

• Interactive lecture and discussion.
• Hands-on demonstrations and guided exercises.
• Practical implementation in a sandboxed healthcare simulation environment.

Course Customization Options

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

Ollama is a platform designed for running large language and multimodal models locally, supporting governance and responsible AI practices.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to implement fairness, transparency, and accountability in Ollama-powered applications.

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

• Apply responsible AI principles in Ollama deployments.
• Implement content filtering and bias mitigation strategies.
• Design governance workflows for AI alignment and auditability.
• Establish monitoring and reporting frameworks for compliance.

Format of the Course

• Interactive lecture and discussion.
• Hands-on governance workflow design labs.
• Case studies and compliance-focused exercises.

Course Customization Options

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

Ollama enables the local execution of large language and multimodal models while facilitating secure deployment strategies.

This instructor-led live training, available online or on-site, targets intermediate professionals seeking to deploy Ollama with robust data privacy and regulatory compliance measures.

Upon completion, participants will be capable of:

• Securely deploying Ollama within containerized and on-premises environments.
• Utilizing differential privacy techniques to protect sensitive data.
• Establishing secure logging, monitoring, and auditing protocols.
• Enforcing data access controls that align with regulatory requirements.

Course Format

• Interactive lectures and discussions.
• Practical labs focused on secure deployment patterns.
• Case studies and hands-on exercises centered on compliance.

Customization Options

• For tailored training arrangements, please contact us.

Ollama is a platform designed to run large language models and multimodal applications locally.

This instructor-led live training, available either online or on-site, is tailored for intermediate practitioners looking to master prompt engineering techniques to enhance the quality of Ollama outputs.

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

• Create effective prompts for a variety of use cases.
• Apply strategies such as priming and chain-of-thought structuring.
• Implement prompt templates and manage context effectively.
• Develop multi-stage prompting pipelines for complex workflows.

Course Format

• Interactive lectures and discussions.
• Practical exercises focused on prompt design.
• Hands-on implementation within a live lab environment.

Customization Options

• To arrange a customized training session, please contact us.

Ollama is a platform designed for running large language and multimodal models locally and at scale.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced engineers looking to scale Ollama deployments in multi-user, high-throughput, and cost-effective environments.

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

• Configure Ollama for multi-user and distributed workloads.
• Optimize GPU and CPU resource allocation.
• Implement strategies for autoscaling, batching, and reducing latency.
• Monitor and optimize infrastructure for enhanced performance and cost efficiency.

Course Format

• Interactive lectures and discussions.
• Hands-on labs for deployment and scaling.
• Practical optimization exercises in live environments.

Course Customization Options

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

Ollama serves as a platform that allows users to run and fine-tune large language and multimodal models on their own hardware.

This guided, live training session (available online or in-person) is designed for experienced ML engineers, AI researchers, and product developers who want to construct and deploy multimodal applications utilizing Ollama.

Upon completing this training, participants will be capable of:

• Configuring and executing multimodal models via Ollama.
• Combining text, image, and audio inputs for practical applications.
• Creating document analysis and visual question-answering systems.
• Developing multimodal agents that can reason across different data types.

Course Format

• Interactive lectures and discussions.
• Practical exercises using real-world multimodal datasets.
• Live laboratory sessions focused on implementing multimodal pipelines with Ollama.

Customization Options

• To arrange a tailored training session for this course, please get in touch with us.

Advanced Ollama Model Debugging and Evaluation is an intensive course designed to help participants diagnose, test, and measure the behavior of models within local or private Ollama deployments.

This instructor-led live training, available online or on-site, is tailored for advanced AI engineers, ML Ops professionals, and QA specialists who aim to ensure the reliability, accuracy, and operational readiness of Ollama-based models in production environments.

Upon completing this training, participants will be able to:

• Systematically debug Ollama-hosted models and reliably reproduce failure scenarios.
• Design and implement robust evaluation pipelines utilizing both quantitative and qualitative metrics.
• Implement observability solutions (logs, traces, metrics) to monitor model health and detect drift.
• Automate testing, validation, and regression checks integrated into CI/CD pipelines.

Course Format

• Interactive lectures and discussions.
• Hands-on labs and debugging exercises focused on Ollama deployments.
• Case studies, group troubleshooting sessions, and automation workshops.

Customization Options

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

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

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

• Establish an efficient environment for fine-tuning AI models on Ollama.
• Prepare datasets suitable for supervised fine-tuning and reinforcement learning.
• Optimize AI models to enhance performance, accuracy, and efficiency.
• Deploy customized models within production environments.
• Assess model improvements and guarantee robustness.

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

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

• Set up and deploy LLMs using Ollama.
• Optimise 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 Patras (offered online or onsite) is targeted at beginner-level professionals who wish to install, configure, and utilize Ollama for operating AI models on their local machines.

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

• Comprehend the fundamentals of Ollama and its operational capabilities.
• Establish Ollama for executing local AI models.
• Deploy and interact with LLMs using Ollama.
• Optimize performance and resource allocation for AI workloads.
• Explore use cases for local AI deployment across various industries.

Ollama is an open-source utility designed to run large language models locally on both consumer and enterprise hardware. By encapsulating model quantization, GPU allocation, and API serving into a single command-line interface, it empowers organizations to host LLMs such as Llama, Mistral, and Qwen independently, thereby eliminating the need to transmit prompts or data to providers like OpenAI, Anthropic, or Google.