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Issue #136
This week in Issue #136:
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If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
A Time Series DB from Scratch
An interesting article that showcases how to implement a time series database from scratch - the tutorial uses golang but proposes a generic architecture that can be implemented in any language.
This overview contains a showcase that shows the speeds that Julia can achieve, which are compared to FORTRAN equivallent, emphasising on the speed benefits whilst having a high level programming interface.
The Data Exchange podcast brings a conversation with Prefect CTO Chris White, where they talk about all-things-data-pipelines. They dive into the challenges, components, architectures and use-cases of ETL pipelines in production.
A comprehensive compilation of perspectives from thought leaders in the deep learning space including Yoshua Bengio, Geoffrey Hinton and Yann LeCun, covering trends and insights on the future of deep learning.
One of the most interesting white papers so far, written by Immuta's Chief Privacy Officer Andrew Burt. This paper covers critical topics on privacy and cybersecurity, as well as how these topics have been changing as we move into massive scale production systems. This paper also provides great historical case studies that provide an insight of how important conceptual shifts and standardisation of thses concepts will be.
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