Issue #168 - THE ML ENGINEER ??

This #168 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 ??

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This week in Issue #168:?

• Kompute Promoted to Incubation
• Modern C++ Computer Vision
• Another Road to MLOps Mastery
• Extending Kubernetes for MLOps
• Airflow Unbundling ETL at Scale
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more ??

If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to [email protected]! We have received a lot of great suggestions in the past, thank you very much for everyone's support!

Kompute Promoted to Incubation

Kompute is promoted to Incubation level at the Linux Foundation ?? Kompute is an open source project that enables developers to introduce GPU acceleration to their projects across 1000s of cross-vendor GPUs (NVIDIA, AMD, Qualcomm & Friends) for machine learning and advanced data processing use-cases. This reflects the matury and adoption of Kompute across the OSS community.

Modern C++ Computer Vision

The University of Bonn has released a fantstic resource for practitioners to learn?Modern C++ for Computer Vision covering key foundational and advanced concepts through 10 video lectures, including build tools, core libraries, programming concepts, memory management and hands on projects.

Another Road to MLOps Mastery

Another useful resource from the MLOps community that provides a suggested set of resources that can be used for progressive learning to build a deeper understanding on core MLOps concepts and best practices.

Extending Kubernetes for MLOps

This article provides insights from various practitioners and technology leaders in the machine learning space on how Kubernetes is currently being used and extended to support the key funcionalities required for production-grade machine learning at scale.

Airflow Unbundling ETL at Scale

An interesting article that explores the architectural and practical challenges that rise when adopting ETL at scale, and covers the way they introduced some solution such as delegating processing into 3rd party systems for relevant processing.

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:

• 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!

About us: The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world-class research into responsible machine learning.

? Check out our website