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THE ML ENGINEER 🤖
Issue #158
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We wish happy holidays to all our MLE Newsletter subscribers!!
🎉🎇🎁🎊🎈⛄❄🥳
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This week in Issue #158:
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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The lifecycle of a machine learning model only begins once it's in production. Our hands on article covers end to end principles, patterns and techniques around production machine learning monitoring, including the code to deploy and monitor an ML model using explainability, outlier detection, concept drift and statistical performance techniques.
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The data exchange podcast dives into conversation with Shopify Director of Engineering Azeem Ahmed, and covers key insights from the team he leads developing the APIs used by all internal data scientists.
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As organisations develop their internal capabilities for end to end machine learning, the concept of the canonical stack for machine learning has been growing providing best practices and unified infrastructure architectures leveraging the best available tools for each specialised phase of the ML lifecycle.
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As MLOps practitioners the ecosystem keeps changing, and it is often hard to navigate the best resources to use - this article has found five key books for MLOps and Machine Learning practitioners to develop their skills further for 2022.
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TheNextWeb has put together for the 5th concecutive year an annual set of predictions for the AI ecosystem, gathering views for several experts in the industry.
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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:
- 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.
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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:
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About us |
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The Institute for Ethical AI & Machine Learning is a Europe-based research centre that carries out world-class research into responsible machine learning.
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© 2018 The Institute for Ethical AI & Machine Learning
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