Model Fine-Tuning Training in Riga

This live, instructor-guided training in Riga (delivered online or in-person) is tailored for advanced defense AI engineers and military tech developers seeking to refine deep learning models for autonomous vehicles, drones, and surveillance systems, ensuring compliance with strict security and reliability criteria.

By the conclusion of this program, attendees will be capable of:

• Optimizing computer vision and sensor fusion models for effective surveillance and target acquisition.
• Enabling autonomous AI systems to respond to shifting environments and mission parameters.
• Deploying robust validation and fail-safe safeguards within model pipelines.
• Guaranteeing alignment with defense-specific compliance, safety, and security mandates.

This instructor-led, live training in Riga (online or onsite) is aimed at intermediate-level legal tech engineers and AI developers who wish to fine-tune language models for tasks like contract analysis, clause extraction, and automated legal research in legal service environments.

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

• Prepare and clean legal documents for fine-tuning NLP models.
• Apply fine-tuning strategies to improve model accuracy on legal tasks.
• Deploy models to assist with contract review, classification, and research.
• Ensure compliance, auditability, and traceability of AI outputs in legal contexts.

This live, instructor-led training, held in Riga (either online or on-site), targets intermediate to advanced medical AI developers and data scientists seeking to refine models for clinical diagnosis, disease forecasting, and patient outcome prediction using diverse medical data formats.

By the end of the program, participants will be equipped to:

• Fine-tune AI systems using healthcare datasets such as EMRs, imaging archives, and time-series information.
• Implement transfer learning, domain adaptation, and model compression strategies tailored to medical contexts.
• Manage privacy protocols, mitigate bias, and ensure regulatory compliance throughout the development lifecycle.
• Deploy and continuously monitor fine-tuned models in operational healthcare settings.

This instructor-led live training in Riga (delivered online or onsite) targets advanced data scientists and AI engineers in the financial industry who aim to adapt models for credit scoring, fraud identification, and risk modeling using sector-specific data.

By the conclusion of this program, participants will be capable of:

• Refining AI models with financial datasets to improve fraud and risk forecasting.
• Employing techniques like transfer learning, LoRA, and regularization to increase model efficiency.
• Embedding financial compliance needs into the AI modeling workflow.
• Implementing optimized models for live use in financial services platforms.

This live, instructor-led training in Riga (delivered online or onsite) is tailored for senior AI maintenance engineers and MLOps experts looking to build robust continual learning pipelines and apply effective update strategies to their deployed, fine-tuned models.

Upon completing this training, participants will be able to:

• Architect and deploy continual learning workflows for live model environments.
• Alleviate catastrophic forgetting by employing rigorous training techniques and memory management practices.
• Establish automated monitoring systems that trigger updates in response to model drift or data variance.
• Embed seamless model update strategies within established CI/CD and MLOps infrastructure.

This live, instructor-led program, available in Riga (whether online or onsite), targets intermediate embedded AI developers and edge computing specialists seeking to optimize and adjust lightweight AI models for constrained device environments.

By the conclusion of the session, attendees will be equipped to:

• Choose and adjust pre-trained models appropriate for edge scenarios.
• Utilize quantization, pruning, and other reduction techniques to decrease model size and latency.
• Apply transfer learning to fine-tune models for specific performance goals.
• Deploy optimized models onto actual edge hardware platforms.

This live, instructor-led session in Riga (delivered online or onsite) is designed for senior computer vision engineers and AI developers aiming to optimize VLMs such as CLIP and Flamingo for superior performance in specialized visual-text tasks.

By the conclusion of this program, attendees will be able to:

• Grasp the structural and pretraining aspects of vision-language models.
• Customize VLMs for tasks including classification, retrieval, captioning, or multimodal QA.
• Structure datasets and employ PEFT techniques to lower resource demands.
• Validate and implement tailored VLMs in live production environments.

This live, instructor-led training in Riga, delivered either online or onsite, targets intermediate-level ML engineers and AI compliance professionals. The objective is to equip these professionals with the skills to identify, evaluate, and mitigate safety risks and biases inherent in fine-tuned language models.

By the conclusion of the training, participants will be capable of:

• Understanding the ethical and regulatory frameworks that define safe AI operations.
• Identifying and assessing common bias patterns in fine-tuned models.
• Applying bias mitigation techniques throughout the training lifecycle.
• Designing and auditing models to ensure safety, transparency, and equitable performance.

This instructor-led live training, conducted in Riga (online or onsite), is tailored for intermediate-level NLP engineers and knowledge management teams looking to fine-tune RAG pipelines to improve outcomes in question answering, enterprise search, and summarization.

Upon completion, participants will be equipped to:

• Comprehend the architecture and operational workflows of RAG systems.
• Adapt retriever and generator components for domain-specific datasets.
• Assess RAG performance and implement enhancements using PEFT techniques.
• Roll out optimized RAG systems for internal operations or production environments.

This live, instructor-led session in Riga (delivered online or on-site) targets intermediate machine learning practitioners and AI developers aiming to fine-tune and deploy open-weight models such as LLaMA, Mistral, and Qwen for specific business or internal use cases.

Upon completion, participants will be able to:

• Understand the ecosystem and key differences among open-source LLMs.
• Prepare datasets and fine-tuning configurations for models like LLaMA, Mistral, and Qwen.
• Execute fine-tuning pipelines using Hugging Face Transformers and PEFT.
• Evaluate, save, and deploy fine-tuned models in secure environments.

This live, instructor-led session in Riga, offered either online or in person, caters to intermediate data scientists and AI engineers seeking to optimize LLM fine-tuning for cost and efficiency through methods like LoRA, Adapter Tuning, and Prefix Tuning.

By the end of the session, attendees will be able to:

• Comprehend the underlying theory of parameter-efficient fine-tuning strategies.
• Execute LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
• Evaluate the trade-offs in performance and cost between PEFT methods and full fine-tuning.
• Scale and deploy fine-tuned LLMs with lower compute and storage overhead.

This instructor-led, live session in Riga (available online or onsite) is designed for intermediate to advanced machine learning engineers, AI developers, and data scientists aiming to leverage QLoRA for the efficient refinement of large models toward specific objectives and custom needs.

By the conclusion of this program, attendees will be equipped to:

• Comprehend the theoretical basis of QLoRA and quantization approaches for LLMs.
• Execute QLoRA to fine-tune large language models for sector-specific uses.
• Maximize fine-tuning efficiency on limited hardware via quantization.
• Effectively deploy and measure fine-tuned models in real-world scenarios.

This live, instructor-led course in Riga (online or onsite) targets advanced machine learning engineers and AI researchers who aim to utilize RLHF for fine-tuning large AI models to achieve optimal performance, safety, and alignment.

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

• Comprehend the core theories of RLHF and its significance in modern AI development.
• Create and train reward models based on human input to guide reinforcement learning.
• Apply RLHF methods to fine-tune large language models, ensuring alignment with human preferences.
• Execute best practices for scaling RLHF processes for robust, production-ready AI systems.

This instructor-led, live training in Riga (delivered online or onsite) is tailored for intermediate-level professionals aiming to develop practical skills in adapting AI models for critical financial duties.

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

• Comprehend the basics of fine-tuning AI for financial applications.
• Apply pre-trained models to specific tasks within the finance domain.
• Deploy techniques for detecting fraud, assessing risk, and creating financial advice.
• Guarantee adherence to financial regulations like GDPR and SOX.
• Embed data security and ethical AI protocols into financial systems.

Designed for advanced professionals, this instructor-led, live training session (available online or onsite) focuses on sharpening the ability to diagnose and overcome fine-tuning challenges in machine learning models.

By the end of the program, participants will possess the ability to:

• Accurately diagnose issues stemming from overfitting, underfitting, and data imbalance.
• Implement robust strategies to enhance model convergence.
• Optimize fine-tuning pipelines for improved overall performance.
• Apply practical tools and techniques to debug complex training processes.

This live, instructor-led training in Riga (online or on-site) is designed for senior-level professionals seeking to master the techniques required for cost-efficient fine-tuning of large models in practical, real-world settings.

Upon completion of this training, participants will have the ability to:

• Grasp the specific challenges associated with fine-tuning large models.
• Implement distributed training methodologies applicable to large models.
• Utilize model quantization and pruning to enhance efficiency.
• Improve hardware utilization for dedicated fine-tuning workflows.
• Successfully deploy fine-tuned models within production environments.

This instructor-led, live training in Riga (offered online or onsite) is designed for intermediate-level professionals aiming to utilize prompt engineering and few-shot learning to enhance LLM performance in practical, real-world applications.

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

• Comprehend the underlying principles of prompt engineering and few-shot learning.
• Develop effective prompts for diverse NLP tasks.
• Apply few-shot techniques to adapt LLMs with limited data resources.
• Optimize LLM performance for specific operational needs.

This instructor-led, live course in Riga (available online or in-person) is intended for advanced professionals aiming to excel in multimodal model refinement for advanced AI solutions.

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

• Comprehend the internal architecture of prominent multimodal models like CLIP and Flamingo.
• Effectively manage the preparation and preprocessing of multimodal datasets.
• Apply fine-tuning techniques to align models with specific task requirements.
• Optimize model performance and suitability for real-world applications.

This live, instructor-led training is delivered in Riga (available online or on-site) and targets advanced AI researchers, machine learning engineers, and developers seeking to fine-tune DeepSeek LLM models. The goal is to develop specialized AI applications customized for particular industries, domains, or business goals.

By the end of this program, participants will possess the ability to:

• Comprehend the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3.
• Prepare and preprocess datasets specifically for fine-tuning tasks.
• Apply fine-tuning techniques to DeepSeek LLMs for domain-specific use cases.
• Efficiently optimize and deploy the fine-tuned models.

This live, instructor-led training session, offered either online or onsite at Riga, is designed for advanced professionals seeking to deploy fine-tuned models with precision and efficiency.

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

• Understand the specific challenges associated with deploying fine-tuned models in production settings.
• Containerize and deploy models leveraging industry-standard tools like Docker and Kubernetes.
• Set up comprehensive monitoring and logging systems for active models.
• Refine models for optimal latency and scalability in practical, real-world contexts.

This instructor-led, live session in Riga (delivered online or on-site) targets advanced machine learning practitioners aiming to master cutting-edge transfer learning techniques and apply them to intricate real-world scenarios.

By the conclusion of this course, participants will be capable of:

• Comprehending the advanced theories and practices of transfer learning.
• Applying domain-specific adaptation methods to pre-trained models.
• Utilizing continual learning to address evolving tasks and data streams.
• Mastering multi-task fine-tuning to improve performance across various tasks.

This live, instructor-led training in Riga focuses on advanced fine-tuning and prompt governance in Vertex AI, assisting intermediate and advanced professionals in refining Gemini models, establishing robust evaluation protocols, and deploying dependable generative AI solutions for real-world business applications.

This live, instructor-guided training, available in Riga (online or on-site), is tailored for machine learning professionals from beginner to intermediate levels who aim to comprehend and implement transfer learning techniques to boost the efficiency and performance of their AI projects.

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

• Grasp the fundamental principles and advantages associated with transfer learning.
• Investigate widely adopted pre-trained models and their various use cases.
• Execute the fine-tuning process of pre-trained models tailored for specific objectives.
• Leverage transfer learning to tackle practical challenges in Natural Language Processing (NLP) and computer vision.

This live, instructor-led training conducted in Riga (either online or onsite) is tailored for intermediate-level developers and AI professionals who aim to implement fine-tuning strategies for large models without relying on extensive computational resources.

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

• Comprehend the underlying principles of Low-Rank Adaptation (LoRA).
• Execute LoRA for the efficient fine-tuning of large models.
• Optimize fine-tuning workflows for resource-limited environments.
• Test and deploy LoRA-modified models for practical use cases.

This live training session, delivered either online or on-site in Riga, is tailored for intermediate to advanced professionals seeking to adapt pre-trained models for specific tasks and datasets.

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

• Comprehend the underlying principles of fine-tuning and its various applications.
• Prepare datasets for the fine-tuning of pre-trained models.
• Fine-tune large language models (LLMs) to handle NLP tasks.
• Optimize model performance and resolve frequent challenges.

This instructor-led, live training in Riga (online or onsite) is designed for intermediate-level professionals seeking to strengthen their NLP projects through the effective fine-tuning of pre-trained language models.

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

• Grasp the core principles of fine-tuning for NLP tasks.
• Adapt pre-trained models like GPT, BERT, and T5 for specific NLP use cases.
• Tune hyperparameters to enhance model performance.
• Assess and deploy fine-tuned models in production environments.