Online or onsite, instructor-led live Ollama training courses demonstrate through interactive hands-on practice how to use Ollama to run, fine-tune, and deploy local AI models efficiently.
Ollama training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Rome onsite live Ollama trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Rome
NobleProg Rome, Via Clelia Bertini Attilj 4, Rome, italy, 34D
CoworkTalenti is located in the north part of Rome, in the discrictTalenti-Montesacro, a few minutes from the highway ring. If you go on via Nomentana, you can reach easily the location.
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 allows running large language and multimodal models locally while supporting secure deployment strategies.
This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to deploy Ollama with strong data privacy and regulatory compliance measures.
By the end of this training, participants will be able to:
Deploy Ollama securely in containerized and on-prem environments.
Apply differential privacy techniques to safeguard sensitive data.
Implement secure logging, monitoring, and auditing practices.
Enforce data access control aligned with compliance requirements.
Format of the Course
Interactive lecture and discussion.
Hands-on labs with secure deployment patterns.
Compliance-focused case studies and practical exercises.
Course Customization Options
To request a 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 an in-depth course focused on diagnosing, testing, and measuring model behavior when running local or private Ollama deployments.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI engineers, ML Ops professionals, and QA practitioners who wish to ensure reliability, fidelity, and operational readiness of Ollama-based models in production.
By the end of this training, participants will be able to:
Perform systematic debugging of Ollama-hosted models and reproduce failure modes reliably.
Design and execute robust evaluation pipelines with quantitative and qualitative metrics.
Implement observability (logs, traces, metrics) to monitor model health and drift.
Automate testing, validation, and regression checks integrated into CI/CD pipelines.
Format of the Course
Interactive lecture and discussion.
Hands-on labs and debugging exercises using Ollama deployments.
Case studies, group troubleshooting sessions, and automation workshops.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in Rome (online or onsite) is aimed at advanced-level professionals who wish to fine-tune and customize AI models on Ollama for enhanced performance and 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 Rome (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 Rome (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 Rome (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.
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