When ML models need to be regularly updated in production, a host of challenges emerges. Paramount among ML reproducibility concerns are the following:

• Effectively versioning your models
• Capturing the exact steps in your data munging and feature engineering pipelines
• Dependency management (including of your data and infrastructure)
• Configuration tracking

No one tool can do it all for you – organizations using a mix of Git, Makefiles, ad hoc scripts and reference files for reproducibility. The following overview explains how DVC enters this mix offering a cleaner solution, specifically targeting data science challenges: First Impressions of Data Science Version Control (DVC) (full tutorial)

I recently created [flowpoints](flowpoints.io). This is a tool in which users can create deep learning models in a flowchart kind of manner.

Models can easily be shared, and the corresponding python code is output to the user immediately.

Hope you like it!

Repo

Search-based 3D Planning and Trajectory Optimization for Safe Micro Aerial Vehicle Flight Under Sensor Visibility Constraints

The problem being tackled: Safe navigation of Micro Aerial Vehicles (MAVs) requires not only obstacle-free flight paths according to a static environment map, but also the perception of and reaction to previously unknown and dynamic objects. This implies that the onboard sensors cover the current flight direction. Due to the limited payload of MAVs, full sensor coverage of the environment has to be traded off with flight time. Thus, often only a part of the environment is covered

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They present a combined allocentric complete planning and trajectory optimization approach taking sensor visibility constraints into account.

I was struggling to optimise my vision pipeline on iOS. It turns out the throughput of CoreML is really low on small neural network, namely it is not designed to do inference on large stream of data with a small network.

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I tried OpenCV DNN out curiosity, was not expecting much, thought I hit the performance ceiling of iOS device, but it did surprised me and increase the throughput by two fold.

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I also tried running OpenCV DNN on my development computer, and is able to observe similar performance increase. Major deep learning framework seems do not optimise much on CPU inferencing.

Thinking about it, I don’t even know why we listen to music or look at art. To be entertained? What purpose does that achieve? Could that be useful for AIs, or is it just a side effect of human intelligence that doesn’t really do anything?