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Issue #117
This week in Issue #117:
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An interesting excerpt of a lecture series on applied AI in games, which delves into a practical tutorial that teaches how to program your own Starcraft AI client. This resource provides a very comprehensive overview and intuition on how AI systems can be built against games, and although a full reinforcement-AI-based system is not developed within this lesson specifically, it will provide with the tools and directions to develop towards that milestone for anyone interested.
The Netflix infra engineering team shares learnings and best practices from their Cosmos platform, a system that leverages serverless "lambdas" to enable for the processing of resource-intensive algorithms coordinated via complex hierarchical workflows that can last from minutes to "years".
An excellent tutorial that covers a popular trend in the Kubernetes space, applied towards the intuition behind scaling ETL pipelines. It shows how to use the KEDA (Kubernetes Event-Driven Autoscaler) to scale a celery/rabbit-mq based ETL system.
A broad overview of concepts, techniques and results from's production machine learning infrastructure and data science capabilities. It dives into the main "pillars" of mechanisms used to productionise models, as well as tradeoffs, architectures, and lessons learned.
A brief and consise overview of a set of tips, tricks and best practices that can be followed to tackle Kaggle (and general ML) challenges, including correlation matrices, missing values, dataframe styler, and more.
The topic for this week's featured production machine learning libraries is GPU Acceleration Frameworks. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. The four featured libraries this week are:
  • Vulkan Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced  data processing usecases.
  • CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
  • Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
  • CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
If you know of any libraries that are not in the "Awesome MLOps" list, please do give us a heads up or feel free to add a pull request
As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following:
If you know of any guidelines that are not in the "Awesome AI Guidelines" list, please do give us a heads up or feel free to add a pull request!
© 2018 The Institute for Ethical AI & Machine Learning