The MachineLearning EngineerIssue #172

This #172 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 #172:

• Secure ML with MLSecOps
• The State of Data Engineering
• Doordash Declarative Fabricator
• Google Brain Distributed Flow ML
• Hashicorp from CEO to Tech IC
• 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 a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone’s support!

Secure ML with MLSecOps

The operation and maintenance of large scale production machine learning systems has uncovered new challenges in the intersection of machine learning and security. This session will cover practical and conceptual insights related to challenges and solutions to ensure secure machine learning systems at scale.

The State of Data Engineering

As exciting as the MLOps space is, a lot of key challenges in scaling ML systems still lie at the Data Engineering systems and operations; this article covers great insights and trends on the state of data engineering.

Doordash Declarative Fabricator

DoorDash shares how they tackled some of their machine learning challenges at scale through a declarative ML framework, in this article they cover the motivations, challenges, concepts and lessons learned.

Google Brain Distributed Flow ML

Interesting research from Google Brain on dataflow architectures for distributed training in machine learning, showcasing the advantages optimizing for hardware utilization compared to the more traditional push (MPI) architectures.

Hashicorp from CEO to Tech IC

Hashicorp CEO Mitchell Hashimoto joins the Stackoverflow podcast to share insights on how he has been making the shift back to technical roots as technical contributor to once again support on spearheading innovations.

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!