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4THE ML ENGINEER 🤖 Issue #136     This week in Issue #136:   A Time Series DB from Scratch Best of Speed & Simplicity w Julia Towards Next Gen Dataflow The Future of Deep Learning Privacy & Cybersecurity Merging Awesome AI Guidelines to check out this week + more 🚀   Forward  email, or share the online version on 🐦 Twitter,  💼 Linkedin and  📕 Facebook!   If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! 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.       Best of Speed & Simpl. w Julia 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.       Towards Next Gen Dataflow 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.       The Future of Deep Learning 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.       Privacy & Cybersecurity Merging 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.       OSS: GPU Accel. Frameworks   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!        OSS: Awesome AI Guidelines   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:   AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018 From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices   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