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Issue #190
THE ML ENGINEER 🤖
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If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter, 💼 Linkedin and 📕 Facebook!
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This week in the MLE #190:
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!
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Developing practical deep learning knowledge can be challenging with the growing number of educational resources. The FastAI course has established itself as a fantastic resource to develop practical knowledge in real-world machine learning use-cases. The core team has just released the updated v5 version with great new content and resources.
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Productionisation of machine learning models at scale can be challenging, particularly due to the stringent high-performant requirements of large-scale production services. This framework provides an interesting way to implement production machine learning services directly in high performant Golang code, enabling for the high-performance features of the language whilst introducing a tradeoff on conversion from data science tools.
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The Julia language continues to grow in popularity as well as feature stability. The videos from the most recent JuliaCon conference are now released for free. These include updates on the language itself, as well as practical case-studies and implementations of various practical use-cases using the Julia language.
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Identifying best practices and tools for Machine Learning Security is key, which is why we are thrilled to release "The MLSecOps Top 10". This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle. This project aims to provide an evaluation of security vulnerabilities analogous to the "OWASP Top 10 Report" but with a focus on machine learning security. The resources are open source and include examples, tools, best practices and next steps, including our contributions to the Linux Foundation Trusted AI.
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Containers continue to become ubiquitous in the general software and data science space. Often practitioners may wonder how the dev tooling they use on their day to day works under-the-hood. This talk provides an extremelly intuitive explanation of how containers work by building the simplest form of a container from scratch.
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Upcoming MLOps Events
The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below.
Conferences we'll be speaking at:
Other relevant upcoming MLOps conferences:
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Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ⭐ github stars. 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. Four featured libraries in the GPU acceleration space are outlined below.
- 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 open source and open community events that are not listed do give us a heads up so we can add them!
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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:
- MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices.
- 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.
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© 2018 The Institute for Ethical AI & Machine Learning
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The Institute for Ethical AI & Machine Learning
