Machine learning is a practice that is evolving and developing every day. Newfound technologies, inventions and methodologies are being introduced to the community on a daily basis. As ML professionals, we can enrich our knowledge and become better at what we do by constantly learning from each other. But with so many resources out there, it might be overwhelming to choose which ones to stay up-to-date on. So where is the best place to start?
We tapped our experienced team to compile a list of eight ML resources for anyone interested in ML: data scientists, data engineers and more. These include YouTube channels, influencers, communities, blogs, and more. We hope you find this list valuable for your professional development. If you have more to add, feel free to drop us a line and we’ll gladly add it to the list.
1. The AI Epiphany
A popular YouTube channel by Aleksa Gordić. Each video covers a paper, code or other AI-related topic and breaks it down in a clear and easy-to-follow way. Playlists cover topics like Difusion models, Robotics, Computer Vision, AI breakthroughs, and more. The AI Epiphany has 24.8 thousand subscribers and more than 600,000 views just two years in the making.
Another reason to love this source: Explanations and tutorials are presented in Gordić’s soothing voice and the videos are entertaining.
2. Towards Data Science
A Medium-based independent publication that covers all data science-related concepts, like model training, serving, feature engineering, monitoring and more. Consistent reading of articles on Towards Data Science ensures readers stay up-to-date on the latest tools and ideas, broaden their knowledge about concepts and code, and learn new skills and theory from scratch. TWS has 628,000 subscribers and you can subscribe to their weekly newsletter for a curated selection of top articles.
Another reason to love this source: TWS is a good fit whether you are taking your first steps in data science or you already have years of experience under your belt.
An online education company with various tech-related courses. Pluralsight offers two course paths for data scientists and data engineers: Machine Learning / AI and Data Professional. These paths cover topics like “Preparing Data for Machine Learning”, “Scaling scikit-learn Solutions”, “Understanding Machine Learning with Python”, “Build, Train and Deploy Your First Neural Network” and more. Pluralsight currently works with thousands of companies and more than 1,500 authors helped create their courses.
Another reason to love this source: The knowledge is practical and can be applied almost immediately in your day-to-day tasks.
A Medium-based publication and Slack community for lively industry discussions. Unlike TWS, articles on MLOps.community are more carefully curated and provide various perspectives on MLOps best practices. The community is currently approaching 10,000 members, and includes both beginners looking to get started, as well as industry veterans who have seen it all.
Another reason to love this source: The Slack community is particularly active, with many highly engaged members sharing their experiences from the ML trenches. If you have a question about a particular use case or technology stack, this is a great place for it.
5. Two Minute Papers
A very popular YouTube series by Dr. Károly Zsolnai-Fehér (1.19 million followers, 90+ million views since 2006). In the videos, Dr. Zsolnai-Fehér explains the research in a way that is both understandable and enjoyable to everyone. Videos cover topics like ML techniques that paint like famous artists, flying robots, animating digital creatures, light and fluid simulations and walking robots. The videos aren’t just theoretical - they show the real-life application of papers, which makes them even more interesting and engaging.
Another reason to love this source: You can continue the discussion about the videos in online communities on Reddit.
6. Yannic Kilcher on YouTube
A successful YouTube series by Yannic Kilcher where he covers recent developments in Machine Learning research. These include papers, projects and the latest news. Kilcher has 127,000 followers and more than 6 million views since 2013.
Another reason to love this source: Kilcher’s ML News videos, which cover the latest updates in the community.
7. Chip Huyen
Born and raised in Vietnam, Chip Huyen is a computer scientist and influencer who publishes her work on her website, social media, in books, on GitHub and more. Her expertise is in designing Machine Learning systems, which she also teaches at Stanford, but she also covers additional topics on her blog.
Another reason to love this source: You can contact Chip directly, just as long as you follow the guidelines here.
8. MLOps on Discord
A vibrant MLOps online community that enables discussions, consultations and sharing information. Popular channels include: #share-your-work, #tools, #what-are-you-reading, #real-time-ml, and more.
Another reason to love this source: Unlike the other sources we listed, the MLOps community enables synchronous communication for lively brainstorming and conversations.
The ability to learn something new about ML every day is exciting and promising. The plethora of resources available in different mediums - video, chat, blogs, etc. - ensures that we can all find the best resources that fit the way we prefer consuming information. If you enjoy blogs, we also recommend you subscribe to the Iguazio blog, so you can get a weekly dose of updates on MLOps, data science and events.