Alternative products to Amazon Machine Learning
16 alternative and related products to Amazon Machine Learning
A curated collection of machine learning projects
Every day, interesting machine learning projects get posted to GitHub, but they soon disappear into the abyss if you're not quick to bookmark them. I've created a small collection of ML projects that stood out to me from HN, reddit, and GitHub. Preference was given to open source projects witht an online demo. Let me know if I missed any!
Michelangelo, is an internal ML-as-a-service platform that democratizes machine learning and makes scaling AI to meet the needs of business as easy as requesting a ride. Michelangelo enables internal teams to seamlessly build, deploy, and operate machine learning solutions at Uber’s scale.
Machine Learning Playground
Breathtaking visuals for learning ML techniques.
A demo inspired by the Tensorflow playground, but a bit broader in depth and with more customization.
Believe it or not, there was a period of time where not everything in Machine Learning was Deep Learning - and this site covers a good amount of traditional methods that are still quite relevant today.
Feedback is welcome!
250 Machine Learning Conferences of 2018
A curated list of AI, Machine Learning & Blockchain events 🌍
We gathered 360 AI, ML, Blockchain conferences worldwide into a single list - the largest one you can find online. Enjoy 🙏
Radically efficient machine teaching
Prodigy is a new annotation tool for creating training and evaluation data for machine learning models. It comes with an extensible, self-hosted back-end, active learning-powered models that update as you annotate, and a modern web application that helps you stay focused.
Real-time Shake Shack line counter using machine learning
Count is a machine learning company looking to quantify the organic data inside photos, video, and sound. Anybody that has lived in Manhattan understands the dynamics of the Shake Shack line in Madison Square Park. We thought it would be fun to practice our craft quantifying the Shake Shack line in real-time as our first experiment.
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