惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

F
Fortinet All Blogs
Last Week in AI
Last Week in AI
IT之家
IT之家
A
About on SuperTechFans
M
MIT News - Artificial intelligence
Y
Y Combinator Blog
T
The Blog of Author Tim Ferriss
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园 - 三生石上(FineUI控件)
博客园 - 【当耐特】
V
Visual Studio Blog
Microsoft Security Blog
Microsoft Security Blog
博客园_首页
aimingoo的专栏
aimingoo的专栏
The Cloudflare Blog
Vercel News
Vercel News
博客园 - Franky
有赞技术团队
有赞技术团队
B
Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
GbyAI
GbyAI
量子位
云风的 BLOG
云风的 BLOG
T
Tailwind CSS Blog

Runpod Blog.

New Runpod datacenter now live: AP-IN-1 Track GPU spend across your team with Cost Centers The GPU supply supercycle is here. Here’s what AI builders need to know. Community Spotlight: One-click AI image and video generation on Runpod with SwarmUI | Runpod Blog Community Spotlight: LoRA Pilot Data Prep to Inference Introducing the Runpod Assistant: Manage Your Cloud GPU Resources with Natural Language OpenAI's Parameter Golf: Train the Best Language Model That Fits in 16MB on Runpod LLM inference optimization: techniques that actually reduce latency and cost Pruna P-Video and Vidu Q3 public endpoints now available on Runpod Runpod brand spelling guide Quickstart - Runpod Documentation The AI market looks nothing like the narrative Training StyleGAN3 with Vision-Aided GAN on Runpod KoboldAI – The Other Roleplay Front End, And Why You May Want to Use It How to Connect Cursor to LLM Pods on Runpod for Seamless AI Dev Community Spotlight: How AnonAI Scaled Its Private Chatbot Platform with Runpod Prompt Scheduling with Disco Diffusion on Runpod Runpod's Latest Innovation: Dockerless CLI for Streamlined AI Development Run Your Own AI from Your iPhone Using Runpod Introducing Flash: Run GPU workloads on Runpod Serverless: No Docker required Use Claude Code with your own model on Runpod: No Anthropic account required Avoid Errors by Selecting the Proper Resources for Your Pod What hackers built on Runpod at TreeHacks 2026 Easily Back Up and Restore Your Pod with Cloud Sync + Backblaze B2 The Complete Guide to GPU Requirements for LLM Fine-Tuning AI Guides, Tutorials & GPU Infrastructure Insights | Runpod Your first Claude Code project within Runpod: a complete setup guide 10 billion Serverless requests and counting Building for resilience: Runpod’s response to the AWS us-east-1 outage How to Connect Google Colab to Runpod
Runpod Partners with Data Science Dojo To Provide Compute...
Brendan McKeag · 2023-09-20 · via Runpod Blog.

Runpod is delighted to collaborate with Data Science Dojo to offer a robust computing platform for their Large Language Model bootcamps. Leveraging our cutting-edge cloud services, Runpod empowers DSD's boot camp participants with a high-performance computing environment, enhancing the efficacy and competitiveness of their learning experience. You can check our need dedicated partner page on their website on Data Science Dojo's Runpod partner page.

Data Science Dojo's Mission

Data Science Dojo is dedicated to providing data science education that is easy to understand, digestible, and engaging. Data is an inextricable, foundational part of AI and machine learning. Models are only as good as the data they are trained on, and training a new model can quickly become expensive if the scope of the project is not appropriately curated and narrowed down. The discipline of data science enables an organization to extract trends, knowledge, and insights from chaotic or unstructured data, enabling a smarter and more sensible use of AI applications.

Data Science Dojo offers a wide variety of data science curricula aimed at both professionals and learners, with both in-person and online boot camps and training sessions focusing on large language models, Power BI, and Python. Their services are trusted by FAANG companies including Facebook, Google, and Amazon along with over 2,500 other enterprises.

Large Language Model Bootcamps by Data Science Dojo

Data Science Dojo offers comprehensive LLM bootcamps that include the following:

  • Generative AI and LLM Fundamentals: A comprehensive introduction to the fundamentals of generative AI, foundation models and Large language models
  • Canonical Architectures of LLM Applications: An in-depth understanding of various LLM-powered application architectures and their relative tradeoffs
  • Embeddings and Vector Databases: Hands-on experience with vector databases and vector embeddings
  • Prompt Engineering: Practical experience with writing effective prompts for your LLM applications
  • Orchestration Frameworks: LangChain and Llama Index: Practical experience with orchestration frameworks like LangChain and Llama Index
  • Deployment of LLM Applications: Learn how to deploy your LLM applications using Azure and Hugging Face cloud
  • Customizing Large Language Models: Practical experience with fine-tuning, parameter efficient tuning and retrieval parameter-efficient + retrieval-augmented approaches
  • Building An End-to-End Custom LLM Application: A custom LLM application created on selected datasets

These kinds of bootcamps are important as LLMs are very costly to train, with the largest models on the market easily running into millions of dollars. An education in data science ensures that the best "ingredients" go into this expensive training process for the highest possible return on time and investment. Ensuring that models are trained with curated and well-understood data as well as a strong foundation on the inner workings of LLMs leads to lowered training costs and greener, more sustainable procedures from fewer GPU hours being required.

How Runpod Can Be Leveraged for LLM Applications

Large language models are notoriously VRAM hungry, with 70b or larger parameter models requiring at least two A100s to load. Training models is even more so, often requiring full pods involving several H100 or A100 cards. The scalable nature of Runpod services allow users to work with any size model or project, where higher spec cards can be added at a moment's notice as necessary. Runpod can be used for an extremely granular level of control over the size and scope of LLM training, as opposed to investing in purchasing a large farm of GPUs without being sure whether the investment will pay off or not. Because there is so much control over the level of compute provided through Runpod's services, it leads to a "just right" fit for the LLM training process every time.

How to Learn More With Data Science Dojo

If you are interested in furthering your education in LLMs, then we highly suggest registering for one of their bootcamps and joining their Discord server. Runpod believes very strongly in the importance of learning in AI's emerging landscape and is thrilled to be a part of DSD's educational process.

Author profile: Brendan McKeag