This is one of the most comprehensive engineering blog posts on how Uber uses Machine Learning (ML) at scale. It covers:
- Uber’s ML Platform – Michael Angelo
- Uber’s research and production efforts and how they inter-relate
- How Uber achieves Model Developers Velocity
I made a list of few terms and concepts from the article:
- ML deployment use cases
- Pervasive deployment of ML in several applications
- Distributed training of ML
- Aligning ML applications with Uber’s priorities
- ML tools across the company (where and what)
- Internal events like – ML conferences, ML reading groups, talk series
- Data Science Workbench (a tool to build and iterate ML models)
- ML Platform team and how they work to support ML development inside Uber
- Technology stacks – Spark, Cassandra, Python and others
- Experiments with external tools both open source and commercial
- Uber’s open source contributions
It is nice to know how a dynamic company uses Machine Learning. There is a lot to learn from here. If you are thinking about building and deploying ML applications Scaling Machine Learning at Uber with Michelangelo | Uber Engineering Blog is a must read. I may go back and read it again.
Here are a few fascinating applications of ML. I mostly track business applications of ML so I was pleasantly surprised to see how Unsupervised Learning and Reinforcement Learning (two ML techniques that do not get much coverage) was being used by two of the biggies in the AI and ML space.
The first two – Depth detection and Nightsight are posts from Google AI Blog. The following concepts were covered in these posts.
Links to articles:
- Under the hood of the Pixel 2: How AI is supercharging hardware – Google AI
- Google AI Blog: Night Sight: Seeing in the Dark on Pixel Phones –
- Horizon: An open-source reinforcement learning platform – Facebook Code
Is manual farming sustainable as the need for agricultural products grow in demand? Can technology help? How does it impact lives of farmers? Is it the right thing to do? Like any other applications of technology, there are more questions than answers. The following links are just a set of leading indicators of trends.
Agricultural vehicles known as “cucumber flyers” enable as many as 50 seasonal workers to harvest crops.
Experts from Fraunhofer IPK in Berlin, along with other German and Spanish researchers, are studying the potential for automating cucumber harvests in the scope of the EU project CATCH, which stands for “Cucumber Gathering – Green Field Experiments.” Project partners are the Leibniz Institute for Agricultural Engineering and Bioeconomy in Germany and the CSIC-UPM Centre for Automation and Robotics (CAR) in Spain.