Large Language Models (LLMs) Training in Tashkent

This instructor-led, live training in Tashkent (online or onsite) is aimed at senior management professionals who wish to understand what LLMs are, explore their potential impact on business operations, and evaluate practical uses of AI tools such as ChatGPT, Microsoft Copilot, or Grok for real-world tasks like content creation, data summarization, and decision support.

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

• Understand what LLMs are and how tools like ChatGPT and Copilot function.
• Use prompt techniques to get practical, reliable results from LLMs.
• Evaluate real use cases such as email drafting, summarizing documents, and productivity automation.
• Identify investment opportunities and strategic applications for AI adoption.

This instructor-led, live training in Tashkent (online or on-site) is designed for senior management teams seeking to understand the strategic value of LLMs and enterprise AI tools. Participants will explore how to integrate these tools into high-level workflows, draft effective prompts, and evaluate opportunities for increased productivity and ROI through AI adoption.

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

• Comprehend the mechanics of LLMs and how tools like ChatGPT and Copilot leverage them.
• Utilize prompt-based interactions to automate and expedite tasks.
• Apply AI tools to practical scenarios such as drafting emails, summarizing reports, and reviewing agreements.
• Assess the strategic advantages, limitations, and licensing aspects of adopting LLMs.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level to advanced-level AI researchers, data scientists, and developers who wish to understand, fine-tune, and implement Meta AI's Large Language Models for various NLP applications.

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

• Understand the architecture and functioning of Meta AI's Large Language Models.
• Set up and fine-tune Meta AI LLMs for specific use cases.
• Implement LLM-based applications such as text summarization, chatbots, and sentiment analysis.
• Optimize and deploy large language models efficiently.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level AI professionals, business analysts, and technology leaders who wish to understand the principles of generative AI and the applications of LLMs in business settings. Participants will learn about transformers, prompt engineering, and ethical considerations in deploying these models for real-world solutions.

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

• Understand the underlying principles of generative AI and large language models.
• Implement and fine-tune LLMs for specific business applications.
• Apply prompt engineering techniques for optimal model outputs.
• Recognize ethical considerations and manage risks in LLM deployment.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level AI professionals and ethicists, data scientists and engineers, and policy makers and stakeholders who wish to understand and navigate the ethical landscape of LLMs.

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

• Identify ethical issues and challenges associated with LLMs.
• Apply ethical frameworks and principles to LLM deployment.
• Assess the societal impact of LLMs and mitigate potential risks.
• Develop strategies for responsible AI development and usage.

This instructor-led, live training in Tashkent (online or on-site) is designed for intermediate-level NLP practitioners and data scientists, content creators and translators, as well as global businesses that aim to leverage LLMs for language translation and the production of multilingual content.

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

• Understand the principles of cross-lingual learning and translation using LLMs.
• Implement LLMs to translate content between various languages.
• Create and manage multilingual datasets for training LLMs.
• Develop strategies to ensure consistency and quality in translation.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate-level financial analysts, data scientists, and investment professionals who wish to leverage LLMs for financial market analysis and prediction.

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

• Understand the application of LLMs in financial market analysis.
• Use LLMs to process financial news, reports, and data for market insights.
• Develop predictive models for stock prices, market trends, and economic indicators.
• Integrate LLM insights into investment decision-making processes.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level environmental scientists and researchers, data analysts, and policy makers and environmental advocates who wish to use LLMs for environmental modeling and analysis.

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

• Understand the application of LLMs in environmental science.
• Utilize LLMs to analyze and model environmental data.
• Interpret LLM outputs for environmental impact assessments.
• Communicate findings effectively to inform policy and conservation efforts.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level VR and AR developers, game designers, and AI engineers who wish to incorporate LLMs into VR and AR applications to create more engaging and responsive environments.

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

• Understand the role of LLMs in creating immersive VR and AR experiences.
• Develop VR and AR applications that utilize LLMs for interactive dialogues and content creation.
• Integrate LLMs with VR and AR development tools for enhanced user engagement.
• Apply best practices for designing AI-driven narratives and interactions in virtual spaces.

Available both online and in Tashkent, this live, instructor-led training is designed for intermediate-level data scientists, machine learning engineers, and software developers who want to apply Large Language Models (LLMs) to multimodal data for advanced AI applications.

Upon completing this training, participants will be able to:

• Understand the core principles of multimodal learning using LLMs.
• Implement LLMs to process and analyze text, image, and audio data.
• Develop applications that leverage the strengths of multimodal data integration.
• Evaluate the performance of multimodal LLM systems.

This instructor-led, live training in Tashkent (online or in-person) is tailored for intermediate-level cybersecurity specialists and data scientists who intend to use LLMs to enhance cybersecurity defenses and threat intelligence.

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

• Understand the role of LLMs in cybersecurity.
• Implement LLMs for threat detection and analysis.
• Utilize LLMs for security automation and response.
• Integrate LLMs with existing security infrastructure.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate-level data scientists and business analysts who aim to leverage large language models (LLMs) to anticipate trends and behaviors across various industries.

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

• Grasp the core principles of LLMs and their significance in predictive analytics.
• Apply LLMs to analyze and forecast data within diverse industry sectors.
• Assess the efficacy of predictive models powered by LLMs.
• Seamlessly integrate LLMs into existing data processing workflows.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level data scientists who wish to gain a comprehensive understanding and practical skills in both Large Language Models (LLMs) and Reinforcement Learning (RL).

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

• Understand the components and functionality of transformer models.
• Optimize and fine-tune LLMs for specific tasks and applications.
• Understand the core principles and methodologies of reinforcement learning.
• Learn how reinforcement learning techniques can enhance the performance of LLMs.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level content creators, marketers, and educational technologists who wish to harness the power of LLMs for generating high-quality, diverse, and engaging content across various domains.

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

• Understand the capabilities of LLMs and their application in content generation.
• Set up and use LLMs for generating various types of content.
• Apply best practices for prompting and fine-tuning LLMs to produce desired outputs.
• Evaluate the quality of AI-generated content and refine it for specific audiences.
• Explore advanced techniques for creative and multi-modal content generation with LLMs.

This instructor-led, live training in Tashkent (online or onsite) is designed for educators, EdTech professionals, and researchers with diverse levels of experience who aim to harness LLMs to create personalized educational experiences.

Upon completing this training, participants will be able to:

• Comprehend the architecture and capabilities of LLMs.
• Identify opportunities for personalizing educational content through LLMs.
• Design adaptive learning platforms that employ LLMs for content personalization.
• Implement LLM-driven strategies to enhance student engagement and learning outcomes.
• Evaluate the effectiveness of LLMs in educational settings and make data-driven decisions for

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level ML practitioners and AI developers who wish to fine-tune and deploy open-weight models like LLaMA, Mistral, and Qwen for specific business or internal applications.

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

• Understand the ecosystem and differences between 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 instructor-led, live training in Tashkent (online or onsite) is designed for beginner to intermediate-level software developers and data scientists who want to implement LLMs in speech recognition and synthesis systems.

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

• Understand the role of LLMs in speech technologies.
• Implement LLMs for accurate speech recognition and natural-sounding speech synthesis.
• Integrate LLMs with speech recognition engines and speech synthesizers.
• Evaluate and improve the performance of speech systems using LLMs.
• Stay informed about current trends and future directions in speech technologies.

This instructor-led, live training conducted in Tashkent (online or on-site) targets beginner to intermediate-level customer support and IT professionals seeking to implement LLMs to build responsive and intelligent customer service chatbots.

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

• Comprehend the fundamentals and architecture of Large Language Models (LLMs).
• Design and integrate LLMs into customer support systems.
• Enhance the responsiveness and user experience of chatbots.
• Address ethical considerations and ensure compliance with industry standards.
• Deploy and maintain an LLM-based chatbot for real-world applications.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate-level data scientists and AI engineers who want to fine-tune large language models in a more cost-effective and efficient manner using techniques such as LoRA, Adapter Tuning, and Prefix Tuning.

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

• Grasp the theoretical foundations of parameter-efficient fine-tuning approaches.
• Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
• Evaluate the performance and cost trade-offs between PEFT methods and full fine-tuning.
• Deploy and scale fine-tuned LLMs while minimizing compute and storage demands.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level to advanced-level machine learning engineers, AI developers, and data scientists who wish to learn how to use QLoRA to efficiently fine-tune large models for specific tasks and customizations.

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

• Understand the theory behind QLoRA and quantization techniques for LLMs.
• Implement QLoRA in fine-tuning large language models for domain-specific applications.
• Optimize fine-tuning performance on limited computational resources using quantization.
• Deploy and evaluate fine-tuned models in real-world applications efficiently.

This instructor-led, live training in Tashkent (online or onsite) targets intermediate-level data and marketing professionals who wish to apply LLMs to analyze and interpret public sentiment from various text sources such as social media posts, product reviews, and customer feedback.

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

• Understand the principles of sentiment analysis and its application using LLMs.
• Preprocess and prepare datasets for sentiment analysis.
• Train and fine-tune LLMs to accurately reflect sentiment in text.
• Analyze sentiment in real-time from social media and other text sources.
• Integrate sentiment analysis findings into business strategies and decision-making processes.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate-level software developers and technical writers who want to leverage LLMs to streamline their coding workflow and create detailed, comprehensive documentation.

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

• Understand the role of LLMs in automating code generation and software documentation.
• Utilize LLMs to create accurate and efficient code snippets and documentation.
• Integrate LLMs into their software development lifecycle for enhanced productivity.
• Maintain high-quality documentation standards using automated tools.
• Address ethical considerations and best practices for using AI in software development.

This instructor-led live training in Tashkent (online or onsite) is tailored for intermediate-level business professionals and data analysts who want to leverage LLMs to extract business insights.

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

• Understand the core principles and applications of LLMs in business intelligence.
• Apply LLMs to analyze large datasets and extract meaningful insights.
• Integrate LLM-driven analytics into strategic business decision-making processes.
• Evaluate the ethical considerations and best practices for using LLMs in business.
• Anticipate future trends in AI and prepare for the evolving landscape of business intelligence.

This instructor-led live training in Tashkent (online or onsite) is designed for intermediate to advanced developers and data scientists seeking to master LlamaIndex for developing innovative LLM-powered applications.

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

• Configure and set up LlamaIndex for integration with LLMs.
• Index and query custom datasets using LlamaIndex to improve LLM performance.
• Design and develop advanced applications that combine LlamaIndex with LLMs.
• Understand and implement best practices for working with LLMs and LlamaIndex.
• Address the ethical implications associated with deploying LLM-powered solutions.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate-level AI researchers, machine learning professionals, and data scientists who aim to leverage LlamaIndex to enhance AI model capabilities, ensuring greater accuracy and reliability for various applications.

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

• Understand the foundational principles and components of LlamaIndex.
• Ingest and structure data for integration with LLMs.
• Implement context augmentation to enhance AI model performance.
• Integrate LlamaIndex into current AI systems and operational workflows.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level professionals who wish to leverage the power of prompt engineering and few-shot learning to optimize LLM performance for real-world applications.

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

• Understand the principles of prompt engineering and few-shot learning.
• Design effective prompts for various NLP tasks.
• Leverage few-shot techniques to adapt LLMs with minimal data.
• Optimize LLM performance for practical applications.

LangGraph is a framework designed for building stateful, multi-actor LLM applications as composable graphs, offering persistent state and precise control over execution.

This instructor-led, live training (available online or on-site) is tailored for intermediate to advanced professionals aiming to design, implement, and operate LangGraph-based legal solutions, ensuring robust compliance, traceability, and governance controls.

Upon completing this training, participants will be able to:

• Design legal-specific LangGraph workflows that maintain auditability and regulatory compliance.
• Integrate legal ontologies and document standards into graph state and processing logic.
• Implement guardrails, human-in-the-loop approvals, and fully traceable decision paths.
• Deploy, monitor, and maintain LangGraph services in production environments with effective observability and cost management.

Course Format

• Interactive lectures and group discussions.
• Extensive hands-on exercises and practical sessions.
• Real-time implementation within a live-lab environment.

Course Customisation Options

• To request a customised version of this training, please contact us to arrange a session.

This instructor-led, live training (online or onsite) is aimed at advanced-level engineers, AI specialists, and localization leads who wish to implement large language model (LLM) systems for automated translation, quality evaluation, and enterprise governance.

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

• Build enterprise-grade LLM localization pipelines integrating open and proprietary models.
• Implement automated QA workflows and quality metrics for translation consistency.
• Establish governance and approval frameworks for multilingual content production.
• Deploy scalable, auditable LLM-based localization systems in secure environments.

AI for SQL refers to the application of artificial intelligence and large language models (LLMs) to automate, optimise, and enhance the generation, execution, and interpretation of SQL queries within enterprise data environments.

This instructor-led, live training (available online or on-site) is designed for intermediate-level data engineers and technical leads who aim to integrate AI capabilities into SQL workflows, enabling natural language querying, intelligent optimisation, and automated data analysis.

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

• Integrate LLMs such as GPT, DeepSeek, LLaMA, Qwen, and Mistral into SQL environments.
• Build natural-language-to-SQL pipelines for conversational data access.
• Implement AI-driven query optimisation and error detection.
• Design secure, auditable AI-SQL workflows for enterprise use.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical sessions.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

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

LangGraph is a framework designed for constructing stateful, multi-actor LLM applications as composable graphs, featuring persistent state and precise control over execution flow.

This instructor-led, live training session (available online or onsite) targets intermediate to advanced professionals who aim to design, implement, and manage LangGraph-based financial solutions with appropriate governance, observability, and compliance.

Upon completing this training, participants will be able to:

• Design finance-specific LangGraph workflows that align with regulatory and audit requirements.
• Integrate financial data standards and ontologies into graph state and tooling.
• Implement reliability, safety, and human-in-the-loop controls for critical processes.
• Deploy, monitor, and optimize LangGraph systems to enhance performance, reduce costs, and meet SLAs.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Vertex AI offers robust tools for creating multimodal LLM workflows that seamlessly integrate text, audio, and image data into a unified pipeline. Leveraging long context window capabilities and Gemini API parameters, it facilitates the development of sophisticated applications focused on planning, reasoning, and cross-modal intelligence.

This instructor-led live training (available online or onsite) is designed for intermediate to advanced practitioners aiming to design, build, and optimize multimodal AI workflows within Vertex AI.

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

• Utilize Gemini models to handle multimodal inputs and outputs.
• Implement long-context workflows to tackle complex reasoning tasks.
• Design pipelines that effectively combine text, audio, and image analysis.
• Optimize Gemini API parameters to enhance performance and cost-efficiency.

Course Format

• Interactive lectures and discussions.
• Practical labs focused on multimodal workflows.
• Project-based exercises applying multimodal use cases.

Course Customization Options

• To request a customized version of this training, please contact us to arrange.

LangGraph enables stateful, multi-actor workflows driven by large language models (LLMs), offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, interoperability, and the development of decision-support systems that align with established medical workflows.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced-level professionals seeking to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.

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

• Design healthcare-specific LangGraph workflows with compliance and auditability as core priorities.
• Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
• Apply best practices to ensure reliability, traceability, and explainability within sensitive healthcare environments.
• Deploy, monitor, and validate LangGraph applications in production healthcare settings.

Course Format

• Interactive lectures and discussions.
• Practical exercises featuring real-world case studies.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized version of this training, please contact us to arrange.

This instructor-led, live training (available online or on-site) is designed for intermediate-level AI developers and localization engineers who aim to design scalable, automated translation pipelines using both proprietary and open-source LLMs.

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

• Design and deploy translation workflows using modern LLM frameworks and APIs.
• Integrate open-source and commercial models into scalable translation systems.
• Optimize translation quality through fine-tuning, prompt engineering, and automation.
• Implement cost-efficient and compliant translation infrastructure for enterprise environments.

LangGraph is a framework designed for building stateful, multi-actor LLM applications as composable graphs, offering persistent state and precise control over execution.

This instructor-led, live training (available online or on-site) is tailored for advanced-level AI platform engineers, DevOps professionals specializing in AI, and ML architects who aim to optimize, debug, monitor, and operate production-grade LangGraph systems.

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

• Design and optimize complex LangGraph topologies to enhance speed, reduce costs, and ensure scalability.
• Engineer system reliability through retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
• Debug and trace graph executions, inspect state, and systematically reproduce production issues.
• Instrument graphs with logs, metrics, and traces, deploy them to production environments, and monitor SLAs and operational costs.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and hands-on practice.
• Real-time implementation in a live-lab environment.

Course Customization Options

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

This instructor-led, live training in Tashkent (online or on-site) is designed for intermediate-level developers who wish to learn how to leverage generative AI with LLMs across various tasks and domains.

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

• Explain the concept of generative AI and its underlying mechanisms.
• Describe the transformer architecture that powers LLMs.
• Apply empirical scaling laws to optimize LLMs for different tasks and constraints.
• Utilize state-of-the-art tools and methods to train, fine-tune, and deploy LLMs.
• Discuss the opportunities and risks associated with generative AI for society and business.

Large language models (LLMs) and autonomous agent frameworks such as AutoGen and CrewAI are transforming how DevOps teams automate tasks like change tracking, test generation, and alert triage by mimicking human collaboration and decision-making.

This instructor-led, live training (available online or onsite) is designed for advanced-level engineers who aim to design and implement DevOps automation workflows driven by large language models (LLMs) and multi-agent systems.

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

• Integrate LLM-based agents into CI/CD workflows for intelligent automation.
• Automate test generation, commit analysis, and change summaries using agents.
• Coordinate multiple agents to triage alerts, generate responses, and offer DevOps recommendations.
• Construct secure and maintainable agent-powered workflows using open-source frameworks.

Format of the Course

• Interactive lecture and discussion.
• Ample exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Postgres is a sophisticated open-source relational database that serves as a robust foundation for AI-driven systems and data intelligence applications.

This instructor-led live training, available online or onsite, is designed for database professionals and developers at an intermediate level who aim to integrate, manage, and optimize AI capabilities directly within Postgres.

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

• Set up and configure Postgres extensions tailored for AI workloads.
• Implement embeddings and perform similarity search using pgvector.
• Connect both open-source and proprietary large language models (LLMs) with Postgres to derive real-time insights.
• Optimize Postgres performance to handle AI-driven queries and workflows effectively.

Course Format

• Interactive lectures and discussions.
• Ample exercises and practical practice opportunities.
• Hands-on implementation within a live lab environment.

Course Customization Options

• To request a customized version of this course, please contact us to arrange your specific needs.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers aiming to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered output, while integrating security controls such as input validation, human-in-the-loop oversight, and output guardrails.

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

• Understand the core vulnerabilities of LLM-based systems.
• Apply secure design principles to LLM app architecture.
• Use tools such as Guardrails AI and LangChain for validation, filtering, and safety.
• Integrate techniques like sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.

LangGraph is a graph-based orchestration framework that enables conditional, multi-step LLM and tool workflows, ideal for automating and personalizing content pipelines.

This instructor-led, live training (online or onsite) is aimed at intermediate-level marketers, content strategists, and automation developers who wish to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.

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

• Design graph-structured content and email workflows with conditional logic.
• Integrate LLMs, APIs, and data sources for automated personalization.
• Manage state, memory, and context across multi-step campaigns.
• Evaluate, monitor, and optimize workflow performance and delivery outcomes.

Format of the Course

• Interactive lectures and group discussions.
• Hands-on labs implementing email workflows and content pipelines.
• Scenario-based exercises on personalization, segmentation, and branching logic.

Course Customization Options

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

This instructor-led live training in Tashkent (online or onsite) is designed for intermediate to advanced professionals aiming to customize pre-trained models for specific tasks and datasets.

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

• Grasp the core principles of fine-tuning and its real-world applications.
• Prepare datasets effectively for fine-tuning pre-trained models.
• Fine-tune large language models (LLMs) for NLP tasks.
• Enhance model performance and resolve common challenges.

LangGraph is a framework designed for creating graph-structured workflows involving Large Language Models (LLMs). It facilitates branching, tool utilization, memory management, and controllable execution processes.

This instructor-led live training, available both online and onsite, targets intermediate-level engineers and product teams aiming to merge LangGraph’s graph logic with LLM agent loops. The goal is to develop dynamic, context-aware applications, including customer support agents, decision trees, and information retrieval systems.

Upon completing this training, participants will gain the ability to:

• Design graph-based workflows that effectively coordinate LLM agents, tools, and memory.
• Implement conditional routing, retry mechanisms, and fallback options to ensure robust execution.
• Integrate retrieval methods, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and strengthen agent behavior to ensure reliability and safety.

Course Format

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs within a sandbox environment.
• Scenario-based design exercises and peer reviews.

Course Customization Options

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

This technical course focuses on leveraging large language models (LLMs) to enhance code quality, minimize technical debt, and automate documentation processes for software teams.

Delivered as an instructor-led live training (available online or onsite), it targets intermediate to advanced software professionals seeking to effectively analyze, refactor, and document complex or legacy codebases using LLMs like GPT.

Upon completing this training, participants will be equipped to:

• Leverage LLMs to clarify code logic, dependencies, and structure within unfamiliar repositories.
• Identify and rectify anti-patterns to boost code readability.
• Automate the creation and maintenance of inline comments, README files, and API documentation.
• Seamlessly integrate LLM-generated insights into current CI/CD and code review workflows.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical applications.
• Hands-on implementation within a live laboratory environment.

Customization Options

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

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level data scientists, AI developers, and AI enthusiasts who wish to use LLMs to perform various NLP tasks and create novel and diverse content for different purposes.

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

• Establish a development environment with LLMs and essential tools.
• Expertly perform NLU and NLI tasks with LLMs.
• Extract, infer, and utilize knowledge graphs effectively.
• Generate and manage dialogues using LLMs for conversational applications.
• Evaluate content quality and diversity generated by LLMs and generative AI.
• Apply ethical principles, ensuring fairness and responsible use of LLMs.

LangGraph is a framework designed for constructing graph-structured LLM applications that support planning, branching, tool use, memory, and controllable execution.

This instructor-led live training (available online or onsite) is targeted at beginner-level developers, prompt engineers, and data practitioners who wish to design and build reliable, multi-step LLM workflows using LangGraph.

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

• Explain core LangGraph concepts (nodes, edges, state) and when to use them.
• Build prompt chains that branch, call tools, and maintain memory.
• Integrate retrieval and external APIs into graph workflows.
• Test, debug, and evaluate LangGraph apps for reliability and safety.

Format of the Course

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based exercises on design, testing, and evaluation.

Course Customization Options

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

This instructor-led, live training in Tashkent (online or onsite) is designed for beginner to intermediate developers interested in leveraging Large Language Models for various natural language tasks.

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

• Configure a development environment that incorporates a popular LLM.
• Construct a foundational LLM and fine-tune it using a custom dataset.
• Apply LLMs to diverse natural language tasks such as text summarization, question answering, text generation, and more.
• Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level engineers and architects who wish to use Tencent Hunyuan to deploy large and MoE models with lower latency, stronger throughput, and better cost control.

By the end of this training, participants will be able to: explain Hunyuan production deployment patterns, optimize inference performance, implement batching and quantization strategies, and plan scalable serving operations.

This instructor-led, live training in Tashkent (online or onsite) targets intermediate-level developers, technical product teams, and AI practitioners who wish to utilize Hunyuan models to create multimodal applications for image, 3D, and video generation and delivery.

By the end of this training, participants will be able to: build prompt-based workflows, generate and review multimodal assets, serve outputs through apps or APIs, and connect Hunyuan capabilities to enterprise product stacks.

This instructor-led, live training in Tashkent (online or onsite) is aimed at beginner-level / intermediate-level / advanced-level AI professionals who wish to use MCP to connect AI assistants with external tools, data, and enterprise services.

By the end of this training, participants will be able to: explain MCP concepts, identify key architecture components, set up a basic integration, and apply security good practices.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level enterprise architects who wish to use Model Context Protocol to design secure, scalable, and governable agent integration platforms for enterprise environments.

By the end of this training, participants will be able to: explain MCP architecture and enterprise patterns, design secure integration platforms, apply governance and access controls, and evaluate deployment and scaling options.

This instructor-led, live training in Tashkent (online or on-site) is designed for intermediate-level developers, architects, and platform engineers who wish to use MCP to build reliable servers and clients for enterprise deployment and operations.

By the end of this training, participants will be able to: explain MCP architecture in practice, build production-ready integrations, deploy and monitor MCP services, and apply versioning, resilience, and support patterns.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level IT leaders, compliance professionals, security teams, and enterprise architects who wish to use sovereign AI principles and governance practices to design AI environments that protect sensitive data, support localization requirements, and reduce vendor lock-in.

By the end of this training, participants will be able to: explain sovereign AI concepts, evaluate hosting and governance options, define controls for prompts and logs, and create a practical adoption roadmap.