Large Language Models (LLMs) Training in Tlalnepantla de Baz

This instructor-led, live training in Tlalnepantla de Baz (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 Tlalnepantla de Baz (online or onsite) 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, craft more effective prompts, and assess opportunities to enhance productivity and ROI through AI adoption.

Upon completing this training, participants will be able to:

• Comprehend how LLMs operate and how tools like ChatGPT and Copilot utilize them.
• Leverage 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 implications of adopting LLMs.

This instructor-led, live training in Tlalnepantla de Baz (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 Tlalnepantla de Baz (online or onsite) is designed for AI professionals, business analysts, and technology leaders at an intermediate level who wish to understand the fundamentals of generative AI and its applications in business contexts. Participants will learn about transformers, prompt engineering, and ethical considerations when deploying these models for real-world solutions.

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

• Comprehend the core principles behind generative AI and large language models.
• Implement and fine-tune LLMs for specific business use cases.
• Apply prompt engineering techniques to achieve optimal model results.
• Identify ethical considerations and manage risks associated with LLM deployment.

This instructor-led, live training in Tlalnepantla de Baz (online or onsite) is designed for intermediate-level AI professionals and ethicists, data scientists and engineers, as well as 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 Tlalnepantla de Baz (online or onsite) is aimed at intermediate-level NLP practitioners and data scientists, content creators and translators, and global businesses who wish to use LLMs for language translation and creating multilingual content.

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

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

This instructor-led live training in Tlalnepantla de Baz (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 Tlalnepantla de Baz (online or onsite) targets intermediate-level environmental scientists and researchers, data analysts, and policymakers 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 Tlalnepantla de Baz (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.

This instructor-led, live training in Tlalnepantla de Baz (online or onsite) is designed for intermediate-level data scientists, machine learning engineers, and software developers who wish to apply Large Language Models (LLMs) to multimodal data for advanced AI applications.

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

• Understand the principles of multimodal learning with 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 Tlalnepantla de Baz (online or in-person) is designed for cybersecurity professionals and data scientists at an intermediate level who want to leverage LLMs to strengthen security measures and improve 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.
• Use LLMs for security automation and response.
• Integrate LLMs with existing security infrastructure.

This instructor-led, live training in Tlalnepantla de Baz (online or onsite) is designed for data scientists and business analysts with an intermediate skill level who want to use large language models (LLMs) to forecast trends and behaviors in various industries.

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

• Grasp the core concepts of LLMs and their significance in predictive analytics.
• Apply LLMs to analyze and forecast data across different sectors.
• Assess the performance and effectiveness of predictive models powered by LLMs.
• Seamlessly integrate LLMs into current data processing workflows.

This instructor-led, live training in Tlalnepantla de Baz (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 Tlalnepantla de Baz (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 Tlalnepantla de Baz (online or onsite) is aimed at educators, EdTech professionals, and researchers with varying levels of experience and expertise who wish to leverage LLMs for creating personalized educational experiences.

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

• Understand the architecture and capabilities of LLMs.
• Identify opportunities for personalization in educational content using LLMs.
• Design adaptive learning platforms that utilize LLMs for content personalization.
• Implement LLM-driven strategies for enhancing 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 Tlalnepantla de Baz, available online or onsite, is designed for intermediate-level ML practitioners and AI developers who want 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 Tlalnepantla de Baz (online or onsite) is designed for software developers and data scientists at the beginner to intermediate level who aim 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 in Tlalnepantla de Baz (online or onsite) is aimed at beginner-level to intermediate-level customer support and IT professionals who wish to implement LLMs to create responsive and intelligent customer support chatbots.

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

• Grasp 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 Tlalnepantla de Baz (online or onsite) targets intermediate-level data scientists and AI engineers looking to fine-tune large language models more affordably and efficiently using techniques like LoRA, Adapter Tuning, and Prefix Tuning.

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

• Understand the theory behind parameter-efficient fine-tuning approaches.
• Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
• Compare performance and cost trade-offs of PEFT methods vs. full fine-tuning.
• Deploy and scale fine-tuned LLMs with reduced compute and storage requirements.

This instructor-led, live training in Tlalnepantla de Baz (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 Tlalnepantla de Baz (online or onsite) is designed for 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 Tlalnepantla de Baz (online or onsite) is aimed at intermediate-level software developers and technical writers who wish 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 Tlalnepantla de Baz (online or onsite) is designed for intermediate-level business professionals and data analysts who wish to harness the power of LLMs to extract business insights.

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

• Understand the fundamentals and applications of LLMs in the context of 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 Tlalnepantla de Baz (online or onsite) is tailored for developers and data scientists at intermediate to advanced levels who aim to master LlamaIndex for creating innovative LLM-powered applications.

Upon completing this training, participants will be able to:

• Set up and configure LlamaIndex for use with LLMs.
• Index and query custom datasets using LlamaIndex to enhance LLM functionality.
• Design and develop sophisticated applications that utilize LlamaIndex and LLMs.
• Understand and apply best practices for working with LLMs and LlamaIndex.
• Navigate the ethical considerations involved in deploying LLM-powered applications.

This instructor-led, live training in Tlalnepantla de Baz (online or onsite) targets intermediate-level AI researchers, machine learning professionals, and data scientists who want to utilize LlamaIndex to boost AI model capabilities, enhancing their accuracy and reliability across diverse applications.

Upon completing this training, participants will be able to:

• Grasp the core principles and components of LlamaIndex.
• Ingest and structure data for LLM consumption.
• Deploy context augmentation to optimize AI model performance.
• Integrate LlamaIndex into current AI systems and workflows.

This instructor-led, live training in Tlalnepantla de Baz (online or onsite) targets intermediate-level professionals seeking to leverage prompt engineering and few-shot learning to optimize LLM performance for real-world applications.

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

• Grasp the core principles of prompt engineering and few-shot learning.
• Craft effective prompts tailored to various NLP tasks.
• Utilize few-shot techniques to adapt LLMs with minimal data.
• Optimize LLM performance for practical, real-world applications.

This instructor-led, live training (available online or onsite) is designed for advanced-level engineers, AI specialists, and localization leads who want 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 that integrate open and proprietary models.
• Implement automated QA workflows and quality metrics to ensure translation consistency.
• Establish governance and approval frameworks for multilingual content production.
• Deploy scalable, auditable LLM-based localization systems in secure environments.

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

This instructor-led, live training (available online or onsite) is designed for intermediate-level data engineers and technical leads who want to integrate AI capabilities into SQL workflows to enable natural language querying, intelligent optimization, 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 optimization and error detection.
• Design secure, auditable AI-SQL workflows for enterprise use.

Format of the Course

• Interactive lecture and discussion.
• Lots of 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.

LangGraph serves as a framework designed for constructing stateful, multi-actor LLM applications through composable graphs, enabling persistent state management and precise control over execution flows.

This instructor-led live training, available either online or on-site, targets intermediate to advanced professionals who aim to design, implement, and manage LangGraph-based financial solutions while ensuring proper governance, observability, and compliance.

Upon completing this training, participants will be equipped to:

• Develop finance-specific LangGraph workflows that adhere to regulatory and audit standards.
• Integrate financial data standards and ontologies into graph states and associated tooling.
• Implement robust reliability, safety measures, and human-in-the-loop controls for critical processes.
• Deploy, monitor, and optimize LangGraph systems to achieve desired performance, cost efficiency, and SLAs.

Course Format

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

Customization Options

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

Vertex AI offers robust tools for constructing multimodal LLM workflows, seamlessly integrating text, audio, and image data into a unified pipeline. Featuring support for long context windows and Gemini API parameters, it empowers advanced applications in planning, reasoning, and cross-modal intelligence.

This instructor-led live training, available both online and on-site, is designed for intermediate to advanced practitioners eager to design, build, and optimize multimodal AI workflows within Vertex AI.

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

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

Course Format

• Interactive lectures and discussions.
• Hands-on labs focused on multimodal workflows.
• Project-based exercises applied to real-world multimodal use cases.

Customization Options

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

LangGraph facilitates 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, enabling interoperability, and developing decision-support systems that integrate seamlessly with clinical processes.

This instructor-led, live training (available online or onsite) targets intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.

Upon completing this training, participants will be able to:

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

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises with real-world case studies.
• Implementation practice in a live-lab environment.

Course Customization Options

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

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers and localization engineers who wish 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 creating stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.

This instructor-led, live training (available online or onsite) is aimed at advanced-level AI platform engineers, DevOps for AI, and ML architects who wish to optimize, debug, monitor, and operate production-grade LangGraph systems.

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

• Design and optimize complex LangGraph topologies for speed, cost, and scalability.
• Engineer reliability with retries, timeouts, idempotency, and checkpoint-based recovery.
• Debug and trace graph executions, inspect state, and systematically reproduce production issues.
• Instrument graphs with logs, metrics, and traces, deploy to production, and monitor SLAs and costs.

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.

This live, instructor-led training in Tlalnepantla de Baz (online or onsite) is aimed at intermediate-level developers who want to learn how to leverage generative AI with LLMs for a variety of tasks and domains.

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

• Explain what generative AI is and how it works.
• Describe the transformer architecture that powers LLMs.
• Use empirical scaling laws to optimize LLMs for different tasks and constraints.
• Apply state-of-the-art tools and methods to train, fine-tune, and deploy LLMs.
• Discuss the opportunities and risks of 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 processes like change tracking, test generation, and alert triage by mimicking human-like collaboration and decision-making.

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

By the end 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 for triaging alerts, generating responses, and providing DevOps recommendations.
• Build secure and maintainable agent-powered workflows using open-source frameworks.

Format of the Course

• Interactive lecture and discussion.
• Extensive 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, an advanced open-source relational database, serves as a robust foundation for building AI-powered systems and data intelligence applications.

This instructor-led live training, available both online and onsite, targets intermediate-level database professionals and developers eager to integrate, manage, and optimize AI capabilities directly within Postgres.

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

• Set up and configure Postgres extensions tailored for AI workloads.
• Implement embeddings and similarity search using pgvector.
• Integrate open-source and proprietary Large Language Models (LLMs) with Postgres to gain real-time insights.
• Optimize Postgres performance for AI-driven queries and workflows.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Tlalnepantla de Baz (online or in-person) targets AI developers, architects, and product managers at intermediate to advanced levels who wish to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered output, while incorporating security controls like input validation, human-in-the-loop oversight, and output guardrails.

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

• Grasp the fundamental vulnerabilities inherent in LLM-based systems.
• Implement secure design principles within LLM application architectures.
• Utilize tools such as Guardrails AI and LangChain for validation, filtering, and ensuring safety.
• Incorporate techniques like sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.

This instructor-led, live training in Tlalnepantla de Baz (online or onsite) is designed for intermediate to advanced-level professionals who want to customize pre-trained models for specific tasks and datasets.

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

• Understand the principles of fine-tuning and its applications.
• Prepare datasets for fine-tuning pre-trained models.
• Fine-tune large language models (LLMs) for NLP tasks.
• Optimize model performance and address common challenges.

The course 'LLMs for Code Comprehension, Refactoring, and Documentation' is a technical program designed to apply large language models (LLMs) to enhance code quality, minimize technical debt, and automate documentation tasks for software teams.

This instructor-led, live training session (available online or onsite) targets intermediate to advanced software professionals aiming to leverage LLMs, such as GPT, to analyze, refactor, and document complex or legacy codebases more efficiently.

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

• Utilizing LLMs to clarify code, dependencies, and logic within unfamiliar repositories.
• Spotting and refactoring anti-patterns while enhancing code readability.
• Automating the creation and upkeep of inline comments, README files, and API documentation.
• Incorporating LLM-driven insights into established CI/CD and code review workflows.

Course Format

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

Course Customization Options

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

This live, instructor-led training in Tlalnepantla de Baz (online or onsite) is designed for 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 for building graph-structured LLM applications that support planning, branching, tool use, memory, and controllable execution.

This instructor-led, live training (online or onsite) is aimed 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 session in Tlalnepantla de Baz (online or onsite) targets beginner to intermediate-level developers who wish to utilize Large Language Models for a range of natural language tasks.

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

• Set up a development environment that includes a popular LLM.
• Create a basic LLM and fine-tune it on a custom dataset.
• Use LLMs for different 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 Tlalnepantla de Baz (online or onsite) is aimed at observability and SRE engineers who want to integrate LLMs and AI into their monitoring, alerting, and incident analysis workflows.

This instructor-led, live training in Tlalnepantla de Baz (online or onsite) is aimed at IT support professionals who wish to use AI tools and automation platforms to streamline support processes, standardize responses, and reduce manual documentation effort.

This instructor-led, live training in Tlalnepantla de Baz (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 Tlalnepantla de Baz (online or onsite) is designed for intermediate-level developers, technical product teams, and AI practitioners seeking to use Hunyuan models to develop multimodal applications for generating and delivering images, 3D assets, and videos.

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

Course objectives transform software and AI engineers into legal engineers—professionals capable of constructing AI solutions for legal tasks such as eDiscovery, review, and investigations.

Over the course of two days, participants construct the full core stack: ingesting and extracting complex real-world legal data, searching and retrieving it, adding retrieval-augmented generation (RAG) with citations, maintaining privacy via a local model while proving its security, conducting a defensible AI review with court-ready metrics, and packaging the final result for deployment.

This instructor-led live training in Tlalnepantla de Baz (available online or on-site) is aimed at beginner-level, intermediate-level, or 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 best practices.

This instructor-led live training in Tlalnepantla de Baz (online or onsite) is intended for intermediate-level enterprise architects who want to use the 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 Tlalnepantla de Baz (online or onsite) targets intermediate-level developers, architects, and platform engineers who wish to utilize MCP to construct 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 patterns for versioning, resilience, and support.

This instructor-led, live training in Tlalnepantla de Baz (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.