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Issue #192
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 #192:
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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Data is at the heart of all products and decisions at modern organisations, and the quality of data is vital to long-term success. Linkedin shares insightful lessons on tackling the challenge of data quality at massive scale, providing usecases in context to AI, as well as challenges, architectures, and next steps.
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Design patterns are not just a way to structure code. They also communicate the problem addressed and how the code or component is intended to be used. This blog post explores some interesting design patterns in data and machine learning including practical examples with various datasets, models and frameworks.
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Data debt in machine learning systems is a non-trivial challenge arising in production ML systems. D. Sculley joins the TWIML podcast to dive into the topic of data-centric machine learning and the importance of best practices to reduce legacy debt and management overhead of large-scale long-living changing MLOps / DataOps systems.
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Although data-centered design is becoming growingly popular in ML, it is not a new concept in the general development space. This resource in particular is a great introduction to development with a focus on data processing instead to create more maintainable, more affordable and more efficient scalable systems.
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Data visualisation with languages like python provide a fantastic resource for building robust stories around decisions that need to be made with data. This article provides a practical overview of the frameworks and concepts in data visualiasation with Python, as well as practical code to use when building capturing narratives with data.
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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
