Course Overview:

This course provides a comprehensive introduction to developing applications with Large Language Models (LLMs). Learners will explore how LLMs transform enterprise workflows, improve productivity, and power innovative solutions. Through hands-on labs, participants will work with HuggingFace, Transformers, LangChain, and multimodal architectures to build end-to-end applications. The course equips learners with practical skills to integrate LLMs into data science workflows and achieve efficiency at scale. 

After completing this course, participants will be able to:

  • Find, pull, and experiment with models from the HuggingFace repository and Transformers API.
  • Use encoder models for semantic analysis, embeddings, classification, and zero-shot learning.
  • Apply GPT-style decoder architectures for text generation and translation tasks.
  • Build multimodal applications combining text, audio, and visual data.
  • Scale text generation and deploy LLM-based applications effectively.
  • Orchestrate workflows with LangChain and agentic frameworks to build enterprise-ready AI systems.

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Key Features:

  • Gain hands-on experience with Hugging Face, Transformers, LangChain, and multimodal architectures through guided labs for LLM applications.

  • Master end-to-end LLM workflows, from fundamentals and model training to inference, scaling, orchestration, and enterprise applications.

  • Explore how LLMs enhance customer engagement, automate workflows, and improve analytics through scalable generative AI approaches.

  • Learn through live, instructor-led sessions that combine expert guidance, hands-on practice, theory, and assessments for effective learning.

Who should Attend?

  • Data Scientists and ML Engineers
  • AI/ML Developers exploring LLMs for enterprise use cases
  • Solution Architects designing AI-powered workflows
  • Business Analysts and Technical Leaders seeking applied AI knowledge

Prerequisites:

  • Basic understanding of Python programming .
  • Familiarity with machine learning concepts (preferred) .
  • Prior exposure to NLP is helpful but not mandatory.

Why choose CloudThat as your training partner?

  • Authorized NVIDIA Training Partner with proven expertise in cloud and AI.
  • Industry-Recognized Trainers certified by NVIDIA and experienced in enterprise AI.
  • Hands-On Learning with practical labs and real-world case studies.
  • Customized Learning Paths designed for beginners to advanced AI professionals.
  • Interactive Learning Experience with live sessions, group discussions, and mentorship.
  • Career Support with guidance on AI/ML roles, certification paths, and resume building.
  • Regularly Updated Content aligned with the latest NVIDIA and open-source ecosystem.

Course Outline: Download Course Outline

  • Overview of workshop topics and schedule.
  • Introduction to HuggingFace and Transformers.
  • Discuss how LLMs can enhance enterprise applications.

  • Introduce and motivate the transformer-style architecture from deep learning first principles.
  • Understand input-output processing with tokenizers, embeddings, and attention mechanisms.

  • Profile encoder models for different NLP tasks where they are most useful.
  • Investigate the use of lightweight models for natural language embedding, classification, subsetting, and zero-shot prediction.

  • Introduce GPT-style decoder models for sequence generation and autoregressive tasks.
  • Apply encoder-decoder architectures for applications like machine translation and few-shot task completion.

  • Integrate different data modalities (text, images, audio) into LLM workflows.
  • Explore multimodal models like CLIP for cross-modal learning, visual language models for image question-answering, and diffusion models for text-guided image generation.

  • Explore LLM inference challenges and deployment strategies, including optimized server deployments.
  • Incorporate LLMs into interesting applications that can scale to larger repositories and user bases.

  • Introduce LangChain for LLM orchestration and agentic workflows
  • Investigate use of agentics and tool-calling for integrating natural language with standard applications and data.

  • Build an LLM-based application integrating text generation, multimodal learning, and agentic orchestration.

  • Review key learnings and answer final questions.
  • Earn a certificate upon successful completion.
  • Complete the workshop survey.

Certification Details:

    Learners will receive a CloudThat–NVIDIA Certificate of Completion after successfully completing the course and final project.

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Course ID: 26354

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