   ​ When working with domain experts on a machine learning project, using an active learning pipeline can help save money and time. This case study goes through the steps and considerations when working with external specialists.​ ​ Historically, implementing an active learning pipeline has been something only within reach for corporations with large budgets for …
   ​ DagsHub provides a free remote MLflow Tracking server for experiment tracking and model registry with every repository.​ ​ DagsHub provides a free remote MLflow Tracking server for experiment tracking and model registry with every repository. DagsHub integration with MLflow MLflow, an open-source tool for managing the machine learning lifecycle, enables real-time tracking of various …
Free Remote MLflow Tracking Server with Team-base Access control Read More »
   ​ DagsHub supports DVC 3! You can now version data with DVC 3.0 and host it on our free and fully configured remote object storage or your own S3-compatible storage​ ​ We’re happy to share that DagsHub supports DVC 3! Version data with DVC 3 and host it on DagsHub’s free and fully configured remote …
   ​ OpenAI officially announced DALL·E 3, a modern text-to-image system built natively on ChatGPT to generate artwork by simply talking to the chatbot.​ ​ OpenAI officially announced DALL·E 3, a modern text-to-image system built natively on ChatGPT to generate artwork by simply talking to the chatbot. Generated with DALL-E 3: “A vibrant yellow banana-shaped couch …
   ​ Every machine learning project starts simple – that’s a good thing. But at some point you need to promote it to production-grade. With 3 lines of code, you can get from a notebook to a central source of truth with code, data, models, and experiments which you can share with team members.​ ​ TL;DR …
3 Lines of Code: Notebook to Production-Ready Machine Learning Project Read More »
   ​ Contribute to Machine Learning and AI projects during Hacktoberfest 2023​ ​ We’re excited to share that DagsHub is partnering with Hacktoberfest to celebrate its 10th anniversary! Join us for a month-long celebration of open-source contributions to Machine Learning and AI projects. Gain hands-on experience building datasets, models, pipelines, and more! From non-code contributors to …
The Official Machine Learning and AI Platform of Hacktoberfest 2023 Read More »
   ​ Learn how to create a simple CI/CD training pipeline for your Machine Learning project using AWS SageMaker and DagsHub​ ​ Training models is a time-consuming task, often requiring multiple iterations. While building the algorithms is challenging on its own, setting up the training process requires further understanding of MLOps which is out of scope …
How to Build CI/CD Pipeline for Continuous Training with SageMaker Read More »
   ​ I speak with Jinen Setpal, ML Engineer at DagsHub about actually building, deploying, and monitoring large language model applications. We dive into evaluation methods, ways to reduce hallucinations and much more. We also answer the audience’s great questions.​ ​ In this live episode, I’m speaking with Jinen Setpal, ML Engineer at DagsHub about actually …
🔴 Building, Deploying and Monitoring Large Language Models with Jinen Setpal Read More »
   ​ Learn how to create a simple CI/CD deployment pipeline for your Machine Learning project using AWS SageMaker and DagsHub​ ​ Most Machine Learning models are dynamic. They continuously learn and enhance their performance with additional data. This requires us to constantly update the deployed model. As we know, deployment is a repetitive process. And …
How to Build CI/CD Pipeline for Continuous Deployment with SageMaker Read More »
   ​ We will check how Weights & Biases log prompts, document the model architecture, and effectively record versioned artifacts.​ ​ In the previous article, we explored MLflow‘s support for experiment tracking of LLM applications through logging of prompts and their outputs. Another widely used tool for tracking experiments is Weights & Biases (W&B or WandB). …
LLMOps: Experiment Tracking with Weights & Biases for Large Language Models Read More »
