Advance Your AI Expertise: Build Custom LLMs, Master Evaluation (h2oGPT, GenAI)
β±οΈ Length: 1.8 total hours
β 4.35/5 rating
π₯ 2,089 students
π July 2024 update
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- Course Overview: Bridging the Gap to Advanced AI Mastery – The “Large Language Models – Level 3” course is a masterclass in modern generative AI infrastructure, designed for practitioners who have surpassed foundational concepts and are ready to engage with engineering complexities. This advanced curriculum focuses heavily on the bridge between theoretical deep learning and industrial-scale implementation, emphasizing the July 2024 landscape. By moving beyond the reliance on closed-source APIs, the course empowers students to harness the full potential of open-source ecosystems, focusing on h2oGPT. It provides a strategic roadmap for building, fine-tuning, and maintaining private LLM instances that offer superior data security and control. Throughout the 1.8 hours of training, participants will explore the latest methodologies in model evaluation and deployment, ensuring they are equipped to handle the complexities of enterprise AI environments where performance, safety, and scalability are the primary metrics of success.
- Requirements / Prerequisites: Essential Technical Foundations – To maximize the value derived from this Level 3 module, students must enter with a robust technical foundation, ideally having completed Level 1 and Level 2 or possessing equivalent industry experience in machine learning. Proficiency in Python is an absolute necessity, as the course involves sophisticated scripting and the use of advanced library features. Participants should be well-versed in the Transformer architecture and comfortable working with neural network frameworks like PyTorch. On the hardware side, following the practical demonstrations will require access to a GPU-accelerated environment, such as high-end local workstations or specialized cloud instances like those provided by AWS or Azure. Mathematically, a solid understanding of optimization algorithms, specifically the mechanics of loss functions and gradient-based learning, is essential to grasp the nuances of the fine-tuning techniques discussed in the curriculum.
- Skills Covered / Tools Used: Advanced Engineering and Optimization – This course provides deep technical mastery over the h2oGPT ecosystem, teaching you how to deploy and manage this robust open-source alternative for high-stakes business applications. You will develop specialized expertise in Parameter-Efficient Fine-Tuning (PEFT), with a specific focus on LoRA (Low-Rank Adaptation) and QLoRA, allowing you to adapt massive models to niche datasets with minimal computational overhead. Beyond model training, you will gain hands-on experience with Automated Evaluation Frameworks like RAGAS, which are critical for benchmarking Retrieval-Augmented Generation systems. The curriculum also covers the integration of Vector Databases for semantic search and the application of Model Quantization to optimize inference speed and memory efficiency. By mastering these tools, you will be able to architect complex GenAI pipelines that incorporate custom datasets while maintaining high standards for accuracy, latency, and throughput in production-grade environments.
- Benefits / Outcomes: Professional Authority and Scalable Deployment – Graduates of this course will transition into the elite tier of AI Systems Architects, capable of leading sophisticated LLM projects from conception to deployment. You will gain the ability to conduct rigorous Quantitative Model Evaluations, moving past anecdotal testing to provide verifiable proof of model performance and reliability. This technical authority allows you to provide expert guidance on AI Governance and Ethics, ensuring that your organizationβs custom models adhere to privacy standards and minimize algorithmic bias. Furthermore, you will be prepared to implement Cost-Effective AI Solutions by choosing between open-source fine-tuning and proprietary API integration based on a deep understanding of the technical trade-offs. The ultimate outcome is a professional portfolio featuring a production-ready model that demonstrates your ability to navigate the July 2024 artificial intelligence ecosystem.
- PROS: High-Efficiency Learning – With a total length of 1.8 hours, the course is perfectly tailored for busy professionals who need to acquire high-level skills without wading through dozens of hours of introductory material.
- PROS: Focus on h2oGPT Mastery – Provides specialized knowledge in a leading open-source framework, offering a distinct advantage in private, secure enterprise AI development.
- PROS: Up-to-the-Minute Content – The July 2024 update ensures the content is relevant to the current state of the industry, highlighting the h2oGPT stack popular for private enterprise AI.
- CONS: Steep Learning Curve – The courseβs rapid pace and technical depth assume significant prior knowledge, making it potentially inaccessible for those who haven’t mastered Level 1 and 2 concepts.
Learning Tracks: English,IT & Software,IT Certifications
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