Delivered either online or onsite, these instructor-led live Ollama training sessions use interactive, hands-on exercises to show you how to efficiently run, fine-tune, and deploy local AI models using Ollama.
Ollama training is offered as either "online live training" or "onsite live training." Online live training (also called "remote live training") takes place through an interactive remote desktop. Onsite live training can be conducted locally at your customer premises in Prague or at NobleProg's corporate training centers in Prague.
From Prague Main Train Station (Praha hlavní nádraží)
Take tram 9 from Hlavní nádraží toward Sídliště Řepy.
Get off at Újezd.
Walk about 10 minutes toward Malostranské náměstí / Prokopská.
Continue along Prokopská to 296/8.
Alternative: Take the metro C from Hlavní nádraží → Muzeum, change to metro A → Malostranská, then walk across Malá Strana. This involves more walking.
From Prague Bus Station — Florenc
Take metro B from Florenc toward Zličín.
Get off at Můstek.
Change to metro A toward Nemocnice Motol.
Get off at Malostranská.
Walk approximately 10–15 minutes to Prokopská 296/8.
This instructor-led live training in Prague empowers finance practitioners and IT personnel to implement Ollama locally. Learners will acquire the ability to deploy, configure, and integrate large language models into secure financial workflows, while adhering to compliance and automation best practices.
This live training in Prague empowers healthcare practitioners and IT teams to implement Ollama for local LLM deployment. You will learn to weave AI into clinical workflows, adapt models for specific operational needs, and uphold rigorous data privacy and regulatory compliance within secure healthcare settings.
This instructor-led training in Prague explores responsible AI principles within Ollama, emphasizing fairness, transparency, and accountability. Participants will create governance workflows, apply bias mitigation techniques, and build compliance monitoring frameworks through practical labs and real-world case studies.
This live, instructor-led training in Prague empowers intermediate professionals with the expertise to deploy Ollama securely. Participants will learn to apply differential privacy, establish secure logging practices, and enforce data access controls that meet regulatory compliance standards, ensuring robust performance in on-premises environments.
This live, instructor-led session in Prague assists intermediate professionals in mastering prompt engineering for Ollama. Participants will learn to create effective prompts, utilize chain-of-thought methods, and handle context windows. They will also build multi-stage pipelines and optimize outputs through practical exercises.
This instructor-led training in Prague focuses on scaling Ollama deployments for high-throughput, cost-efficient environments. Participants will explore GPU optimization, Kubernetes deployment, autoscaling, and monitoring strategies to construct resilient LLM infrastructure capable of handling multi-user workloads.
This instructor-led training in Prague guides advanced ML engineers and AI researchers through the process of building multimodal applications with Ollama. Participants will learn to set up local models, integrate text and image inputs, and design visual QA systems and agents for real-world deployment.
This advanced instructor-led training in Prague focuses on diagnosing, testing, and evaluating local Ollama deployments. It covers systematic debugging, robust evaluation pipelines, observability, and automated CI/CD integration to ensure production reliability and operational readiness.
This instructor-led live training in Prague (online or on-site) is designed for advanced professionals who wish to fine-tune and customize AI models on Ollama to improve performance and support domain-specific applications.
By the end 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.
Optimize AI models for performance, accuracy, and efficiency.
Deploy customized models in production environments.
Evaluate model improvements and ensure robustness.
This instructor-led, live training in Prague (online or onsite) is designed for advanced professionals who aim 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 Prague (online or onsite) is designed for intermediate professionals aiming 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 Prague (online or onsite) is aimed at beginner-level professionals who wish to install, configure, and use Ollama for running AI models on their local machines.
By the end of this training, participants will be able to:
Understand the fundamentals of Ollama and its capabilities.
Set up Ollama for running local AI models.
Deploy and interact with LLMs using Ollama.
Optimize performance and resource usage for AI workloads.
Explore use cases for local AI deployment in various industries.
Ollama is an open-source tool designed for running large language models locally on consumer and enterprise hardware. It simplifies model quantization, GPU allocation, and API serving through a single command-line interface, allowing organizations to self-host LLMs like Llama, Mistral, and Qwen without routing prompts or data through OpenAI, Anthropic, or Google.
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