Description for Open Source LLMOps
Executing and Refining Large Language Models: Acquire the knowledge necessary to operate local large language models and enhance their performance through fine-tuning on scalable platforms such as SkyPilot.
Mastery in LLM Architectures and Tools: Attain a comprehensive understanding of LLM architectures, including Transformers, and utilize tools such as LoRAX and vLLM to facilitate efficient deployment.
Studying the Open-Source LLM Ecosystem: Engage in an in-depth examination of the open-source LLM ecosystem by utilizing pre-trained models such as Code Llama, Mistral, and Stable Diffusion, while also exploring sophisticated architectures, including Sparse Expert Models.
Guided LLM Project and Production Deployment: Engage in a structured project aimed at fine-tuning models such as LLaMA and Mistral on bespoke datasets, followed by the efficient scaling and deployment of these models utilizing cloud platforms and model servers.
Level: Beginner
Certification Degree: yes
Languages the Course is Available: 1
Offered by: On edX provided by AI
Duration: 3�6 hours per week 4 weeks (approximately)
Schedule: Flexible
Pricing for Open Source LLMOps
Use Cases for Open Source LLMOps
FAQs for Open Source LLMOps
Reviews for Open Source LLMOps
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Open Source LLMOps
The AI tool focuses on content optimization through AI-driven processes, leveraging NLP, SEO writing, content construction, research tools, content clustering, and AI templates for efficient and effective content creation.
Autogen streamlines large language model application development with its high-level abstraction framework and optimized API, while fostering community collaboration for ongoing improvement.
Learn to leverage Generative AI for automation, software development, and optimizing outputs with Prompt Engineering.
Learn to use the latest LLM APIs, the LangChain Expression Language (LCEL), and develop a conversational agent.
Explore LLM potential, address limitations, devise business strategies, and stay updated on LLM trends for effective implementation in business operations.
Learn about various generative AI models and architectures, the application of LLMs in language processing, and implement NLP preprocessing techniques using libraries and PyTorch.
Gain practical skills and foundational knowledge of generative AI, along with insights from AWS AI practitioners on how companies leverage cutting-edge technology for value generation.
Define Large Language Models and their use cases, explain prompt tuning, and overview tools for Gen AI development at Google.
The course encompasses the following topics: the development of a text processing pipeline, the comprehension of Naive Bayes classifier theory, and the assessment of the efficacy of classification models following training.
Prompt Mixer is a collaborative workstation application tailored for AI development, offering sophisticated version control, AI functionality augmentation, and secure prompt management for optimized development experiences.
Featured Tools
This training offers essential knowledge in the domains of data, computation, cryptography, and programming, with a focus on the Ruby on Rails framework.
The course covers imaging diagnosis, tree-based survival prediction, randomized trial therapy effects estimation, and NLP medical data classification.
Explore AI's applications, benefits, and challenges, with beginner-friendly content and practical insights for professionals and industry leaders.
Develop generative AI capabilities for data analytics. Learn about generative AI to advance your career as a data analyst! No prior experience is required.
Begin your vocation in generative AI data engineering. Obtain the necessary skills to secure a position as a data engineer by acquiring an understanding of generative AI. No prior experience is required.