Applied ML – How Uber Uses Machine Learning at Scale

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.

Applications of ML – Improving Depth detection, Night Sight, Video Play and Notifications

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.

  • PSL Positive shutter lag
  • Motion metering
  • Exposure stacking
  • Astrophotography
  • Super-resolution
  • Auto white balancing and Learning based AWB algorithm
  • Ultra long shot photography
  • Optical Image stabilization

    Facebook Horizon article covered how they were improving user experience with Reinforcement Learning.

  • Improving video playing using Reinforcement Learning (RL)
  • Improving user notifications using Reinforcement Learning

Links to articles:

  1. Under the hood of the Pixel 2: How AI is supercharging hardware – Google AI 
  2. Google AI Blog: Night Sight: Seeing in the Dark on Pixel Phones
  3. Horizon: An open-source reinforcement learning platform – Facebook Code 

Technology in Farming – Robots, Mixed Reality, Machine Learning

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.
During the Hands Free Hectare project, no human set foot on the field between planting and harvest—everything was done by robots. This includes:
  • Drilling channels in the dirt for barley seeds to be planted at specific depths and intervals with an autonomous tractor;
  • Spraying a series of fungicides, herbicides, and fertilizers when and where necessary;
  • Harvesting the barley with an autonomous combine.

How mixed reality and machine learning are driving innovation in farming

The Economist, in its Q2 Technology Quarterly issue, proclaims agriculture will soon need to become more manufacturing-like in order to feed the world’s growing population. Scientific American reports crops will soon need to become more drought resistant in order to effectively grow in uncertain climates. Farms, The New York Times writes, will soon need to learn how to harvest more with less water.