The MachineLearning EngineerIssue #183

This #183 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 10,000+ subscribers. 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 #183:

• Reproducible Deep Learning
• Trends to Watch in June 2022
• Embedding Search at Faceboook
• Fallacies of Distributed Systems
• Github Malware Store Database
• Upcoming MLOps Events
• 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!

Reproducible Deep Learning

Building a deep learning model is a complex task, full of interacting design decisions, data engineering, parameter tweaking, and experimentation. Having access to powerful tools for versioning, storing, and analyzing every step of the process (MLOps) is essential. This is a fantastic course that dives into end-to-end MLOps with practical and indepth lessons on various key topics.

Trends to Watch in June 2022

The explosion of large models continues in the machine learning ecosystem. Several developments this month are especially noteworthy. The O’Reilly team has put together a great compendium of key trends and resources for June 2022, covering machine learning, general programming, security, hardware and more.

Embedding Search at Faceboook

Search in social networks such as Facebook poses different challenges than in classical web search: besides the query text, it is important to take into account the searcher’s context to provide relevant results. The Facebook Engineering & AI team have an interesting paper that covers the architecture and lessons learned introducing embedding-based search retrieval at Facebook.

Fallacies of Distributed Systems

The mass adoption of microservices has forced more engineers to understand the implications of that decision within their systems. This is particularly important for practitioners entering and growing in the MLOps space. This article provides a great overview of the top fallacies generally ignored or downplayed by programmers new to distributed applications.

Github Malware Store Database

To combat the prevalence of malware in the open source ecosystem, GitHub now publishes malware occurrences in the GitHub Advisory Database. These advisories power Dependabot alerts and remain forever free and usable by the community. This is particularly important in the MLOps space, as we have emphasised as part of the rise of MLSecOps.

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:

• PyData London - June 17th-19th @ London [ML Acceleration]
• EuroPython - July 11th-17th @ Dublin[ML Security]

Other relevant upcoming MLOps conferences:

• MLOps World 2022 - June 7th-10th 2022 @ Toronto
• AIIA MLOps Day-2 Summit - June 23rd @ Online
• EuroSciPy - August 29 @ Switzerland
• World Summit AI - 12th-13th October @ Neatherlands
• Kubecon NA - 24th-28th October @ Detroit

Open Source MLOps Tools

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!

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!