DagsHub

Enterprises face increasing challenges in bringing AI to production while maintaining security, compliance, and scalability. Many teams work with unstructured data like computer vision, audio, text, and LLMs, requiring a solution that operates securely on-premise. DagsHub is now integrated with Red Hat OpenShift and OpenShift AI, providing an end-to-end machine learning platform that covers: • Dataset curation and annotation • Experiment tracking and model management • Secure, scalable MLOps workflows With this integration, AI teams can develop, iterate, and deploy models within their own infrastructure without compromising security or performance. Read the full announcement: https://lnkd.in/dPwZffZJ

What a RAG system looks like from the inside

Object detection is going to be pretty much everywhere

LLMs are versatile tools that require specialized training to reach their full potential. Fine-tuning is the process of adapting a general-purpose LLM to excel at specific tasks or within particular domains. Similar to customizing a recipe with unique spices, fine-tuning infuses an LLM with the knowledge and abilities necessary to meet specific organizational needs. Without fine-tuning, LLMs function as broad knowledge bases, often lacking the depth or focus required for practical applications. This can result in irrelevant, inaccurate, or even harmful outputs. In business settings where precision and reliability are paramount, the consequences of an unrefined #LLM can be severe. DagsHub provides a centralized workspace for #datascientists to manage their entire project lifecycle, from #data to models, while fostering open collaboration.

For quite a long time I have been focused on writing a lengthy and detailed article on different approaches to develop a robust ML model one of which is "Continual learning or CL". The idea of CL arises from the fact as to how humans are capable of learning complex matters while preserving the old information. We also tend to leverage the these old information to learn new information quickly. We are adaptable. But it is not the same with the ML systems. They have to be retrained again on a new set of data. This of course is time-consuming and potentially expensive. In AI continual learning is the process of injecting or adding new information to a trained model while preserving the old information, mimicking human cognitive processes. I got an opportunity to write this article on CL with DagsHub along with Michał Oleszak and Daniel Tannor where we explained the various elements involved in CL -- types, approaches, and challenges -- as well as provided a practical approach to learning CL in PyTorch. You will learn a lot of valuable insights from this article. You can find the article link in the comment below.

Why are transformers so good at understanding language? The answer is Self-Attention. Self-Attention lets transformers focus on different parts of the inputs all at once instead of one piece at a time. It's kind of like giving the model the ability to understand the big picture by mapping the relationships between all of the little pieces within the data. And this is how they pick up on complex patterns and connections. One cool detail is that Self-Attention actually lets the model learn about the order and the spacing of the words itself without providing it explicitly. And that's part of why it's so powerful. So in other words, Self-Attention is not just another tool. It's actually what unlocks a lot of the power of modern transformers in LLMs.

We've enhanced our experiment tracking to let you see your model's predictions and outputs as they evolve during training. Visual insight into model behavior is critical, yet often overlooked in ML workflows. So we're introducing an integrated experiment artifacts view on DagsHub. Key benefits: - Real-time visual feedback: Watch your model learn through images, audio, and even 3D visualizations - Comprehensive artifact support: View text, model files, and even CSV files alongside metrics - Seamless integration: Works with the OSS MLflow API you're already using - Coming soon - HTML, Notebooks, artifact diffing and more. How it works: 1. Use mlflow.log_artifacts() to attach files to your experiment 2. Go to the experiments tab in your DagsHub repo 3. Visualize artifacts directly in the experiment view, no context-switching required. As ML practitioners, we know that numbers alone don't tell the whole story. Now you can literally see your model's progress, catching potential issues early and gaining deeper insights. What other visual tools would enhance your ML workflow? Share your thoughts below! Thanks Tal for building, Anna for design, and the entire team for shipping ⛴️🙏. Also, thanks MLflow for being awesome!

Check out this awesome post about image embeddings benefits, industry use cases and best practices. Thanks Ignacio Peletier Ribera

We're very lucky to be working with the top data scientists at MACSO Check out the full case study