DagsHub is looking for a Machine Learning Evangelist Intern to join our rocket ship, be the face of our company, and help us grow DagsHub's worldwide community (over 15,000 strong!). DagsHub is where people build data science projects. We leverage popular open-source tools to version datasets & models, track experiments, label data, and visualize results. We are a product-led-growth company and community is at the heart of our mission and plays a critical role in our future. As a Machine Learning Evangelist, you will play a major role in our orchestra and greatly impact our users' experience and the future of machine learning.

Have you ever wondered why you have to wait for DVC to pull all the files to access a single file? Maybe you have created custom scripts to work around this problem. But what if I tell you there is a better solution for this issue? Direct Data Access makes it fairly easy for you to load single or multiple files from the DagsHub DVC server. It will help you save time, as you won't be pulling the entire dataset to push a single file.

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It is hard to agree on the best tools to serve models in production because each problem is unique, and their solutions have different constraints. Therefore, I wanted to choose a solution or a set of tools that would benefit as many people as possible. The solution should be simple enough so that it takes only a few minutes to whip up a working prototype and serve it online and, if needed, can scale to larger-scale problems. The core component of this solution is the BentoML package. It is one of the latest promising players in the MLOps landscape and has already amassed half a million downloads on GitHub.

As a beginner, it's hard to realize how the end product of your project should look. You start with a basic machine learning pipeline, and as the project evolves, you adjust and enhance the components to meet your golden metric. To communicate your work with the world, you'd like to have a way for people to interact with the model and evaluate its performance. In this blog, we will be learning how to build a Streamlit application using only python and deploy it to a remote Heroku server. . We will use the Pneumonia-Classification project and showcase how to deploy its Streamlit app to the cloud.

Data science and machine learning deal with complex mathematical concepts and programming tools to build the right kind of algorithms for business decisions. Collaborations and discussions while undertaking and building these projects can be of great help for data scientists and machine learning practitioners. Just like GitHub exists for collaborating on software development in an open-source capacity, a 2019-launched platform named DagsHub is becoming increasingly popular for data scientists and machine learning engineers to come together at a common ground to build their work. "It is like GitHub for data science and machine learning," is how DagsHub describes itself. It is a web platform for data version control and collaboration for data scientists and machine learning engineers and is based on open-source tools, optimised for data science and oriented towards the open-source community.

Platforms like GitHub have been tools for version controlling software projects. However, Machine learning projects are faced with new challenges while working with GitHub: "Model & Data version control". GitHub has a strict file limit of 100MB. This means that Data Scientists & ML Engineers will have to improvise in order to work with GitHub as this restriction prevents version control for Large Datasets and Model Weights. The good news is that DAGsHub solves this challenge thereby allowing efficient Version Control for Data Science projects!