Ollama Training in Tashkent

Ollama serves as a lightweight platform designed for executing large language models on local devices.

This instructor-led live training, available both online and onsite, targets intermediate-level finance professionals and IT specialists aiming to implement, customize, and operationalize AI solutions based on Ollama within financial contexts.

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

• Deploy and configure Ollama to ensure secure usage in financial operations.
• Integrate local large language models into analytical and reporting workflows.
• Adapt models to meet finance-specific terminology and task requirements.
• Apply best practices for security, privacy, and regulatory compliance.

Course Format

• Interactive lectures and discussions.
• Practical exercises using financial data.
• Live laboratory implementation of finance-focused scenarios.

Customization Options

• For a customized training program tailored to your needs, please contact us to make arrangements.

Ollama serves as a streamlined platform for executing large language models directly on local infrastructure.

This instructor-led live training, available both online and onsite, targets intermediate-level healthcare professionals and IT teams looking to deploy, customize, and implement Ollama-based AI solutions within clinical and administrative settings.

Upon completing this training, participants will be able to:

• Install and configure Ollama to ensure secure usage in healthcare environments.
• Integrate local LLMs into clinical workflows and administrative processes.
• Customize models to accommodate 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 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 is a platform that enables the local execution of large language and multimodal models while supporting secure deployment strategies.

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

Upon completing this training, participants will be able to:

• Deploy Ollama securely within containerized and on-premises environments.
• Apply differential privacy techniques to protect sensitive data.
• Implement secure logging, monitoring, and auditing practices.
• Enforce data access controls that align with compliance requirements.

Course Format

• Interactive lectures and discussions.
• Hands-on labs featuring secure deployment patterns.
• Compliance-focused case studies and practical exercises.

Customization Options

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

Ollama is a platform that enables running large language and multimodal models locally.

This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to master prompt engineering techniques to optimize Ollama outputs.

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

• Design effective prompts for diverse use cases.
• Apply techniques such as priming and chain-of-thought structuring.
• Implement prompt templates and context management strategies.
• Build multi-stage prompting pipelines for complex workflows.

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises with prompt design.
• Practical implementation in a live-lab environment.

Course Customization Options

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

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

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level engineers who wish to scale Ollama deployments for multi-user, high-throughput, and cost-efficient 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 autoscaling, batching, and latency reduction strategies.
• Monitor and optimize infrastructure for performance and cost efficiency.

Format of the Course

• Interactive lecture and discussion.
• Hands-on deployment and scaling labs.
• Practical optimization exercises in live environments.

Course Customization Options

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

Ollama is a platform that enables running and fine-tuning large language and multimodal models locally.

This instructor-led, live training (online or onsite) is aimed at advanced-level ML engineers, AI researchers, and product developers who wish to build and deploy multimodal applications with Ollama.

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

• Set up and run multimodal models with Ollama.
• Integrate text, image, and audio inputs for real-world applications.
• Build document understanding and visual QA systems.
• Develop multimodal agents capable of reasoning across modalities.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice with real multimodal datasets.
• Live-lab implementation of multimodal pipelines using Ollama.

Course Customization Options

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

Advanced Ollama Model Debugging & Evaluation is a comprehensive course dedicated to diagnosing, testing, and evaluating model behaviour in local or private Ollama deployments.

This instructor-led, live training (available online or on-site) is designed for advanced-level AI engineers, ML Ops professionals, and QA practitioners seeking to ensure the reliability, fidelity, and operational readiness of Ollama-based models in production environments.

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

• Conduct systematic debugging of Ollama-hosted models and reliably reproduce failure modes.
• Design and execute robust evaluation pipelines using both quantitative and qualitative metrics.
• Implement observability (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 using Ollama deployments.
• Case studies, group troubleshooting sessions, and automation workshops.

Course Customisation Options

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

This instructor-led, live training in Tashkent (delivered either online or on-site) is designed for advanced-level professionals seeking to fine-tune and customise AI models on Ollama for improved performance and domain-specific applications.

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

• Set up an efficient environment for fine-tuning AI models on Ollama.
• Prepare datasets for supervised fine-tuning and reinforcement learning.
• Optimise AI models for performance, accuracy, and efficiency.
• Deploy customised models in production environments.
• Evaluate model improvements and ensure robustness.

This instructor-led, live training in Tashkent (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 Tashkent (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 Tashkent (online or onsite) is designed for beginner-level professionals who want to install, configure, and utilize Ollama to run AI models on their local machines.

Upon completing 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.
• Enhance performance and optimize resource usage for AI workloads.
• Investigate use cases for local AI deployment across various industries.

Ollama is an open-source utility designed for executing large language models locally on both consumer-grade and enterprise hardware. It simplifies complex tasks such as model quantization, GPU resource allocation, and API deployment into a single command-line interface. This allows organizations to self-host prominent LLMs like Llama, Mistral, and Qwen, ensuring that prompts and sensitive data are never sent to external providers like OpenAI, Anthropic, or Google.