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Issue #167
This #167 edition of the ML Engineer newsletter contains curated articles, tutorials and blog posts from experienced Machine Learning and MLOps professionals. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions πŸš€
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This week in Issue #167:
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!
The lifecycle of a machine learning model only begins once it’s in production. This week Seldon Engineering Director & IEML Chief Scientist Alejandro Saucedo will be giving a live webinar on production machine learning monitoring covering how to introduce operational and data science monitoring (outliers & drift) at scale.
Systems Design skills are key for ML & MLOps engineers; in this article Amazon Senior ML Engineer covers in detail a systems design interview type question covering an approach to explore the question of designing the infrastructure behind Netflix from scratch, covering the requirements gathering, design, technical considerations, APIs, etc.
Thoughtworks Engineering has put together a comprehensive overview that explores the challenge of developing infrastructure platforms through 7 key principles that identify key important areas that allow for teams to focus on building on functionality that will bring value to the ultimate users.
Uber Engineering has put together an overview of Orbit, their Bayesian time-series analysis and forecasting library. In this article they cover some of the key features as well as provide insights on how Uber built an internal platform to productise their user-base workflows across their organisation.
A very interesting discussion in the Julia community forum providing insights on the state of machine learning in the Julia language, diving into various key questions that highlight some of the key achievements, opportunities and gaps.
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.
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!
Β© 2018 The Institute for Ethical AI & Machine Learning