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Issue #254🤖 
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The ML Engineer this week:
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If you are a Machine Learning Practitioner looking for an interesting opportunity, I'm currently hiring for a few roles including Applied Science Manager, Applied Scientist, Analytics Team Lead, and Customer Analyst - do check it out and do feel free to share broadly!
Text Embeddings Visually Explained: Great article from Cohere covering the intuition behind text embeddings. This article covers the numerical representations of text that capture its meaning visualizations, showcasing a practical example with the Airline Travel Information System dataset. The article demonstrates how embeddings can transform unstructured text into structured data, enabling context-aware search, clustering, and classification. The piece also highlights the benefits of finetuning models on specific data to enhance performance, emphasizing the potential of text embeddings in handling vast amounts of unstructured data for various applications.
Lessons from 20 years of SRE at Google: A very practical and insightful article sharing actionable learnings for the operation of production systems, which include: 1) importance of proportional risk assessment; 2) testing of recovery mechanisms; 3) canary all changes; 3) emergency reversion mechanisms; 4) ensuring diverse communication channels; 5) designing for graceful degradation; 6) emphasizing disaster resilience; 7) automating mitigations; 8) maintaining frequent rollout cadence, and; 9) diversifying infrastructure to prevent single points of failure
Advanced Python Mastery Free Open Course: A fantastic comprehensive exercise-driven course designed for intermediate Python programmers! This course delves deep into advanced programming techniques commonly used in popular libraries and frameworks, including detailed slides, exercises, and solutions. It excludes certain modern features like async and typing but dives into some of the core foundations of the Python language - a very valuable resource for those aiming to elevate their Python skills.
LinkedIn shares interesting insights of their internal fully managed search platform designed to streamline and democratize search integration for product teams. Their system has allowed to offloads setup, maintenance, and operational tasks from application teams, simplifying the previously complex SeaS verticals. In its inaugural year, Hosted-Search onboarded more use cases than the legacy SeaS did throughout its lifetime, and is now also supporting Global Secondary Indexes in Espresso tables, enhancing the overall search experience for LinkedIn members.
Any ML practitioner working with text will be caught by the intricacies of unicode at one point; this article dives into the absolute minimum every developer should know about unicode. This article dives into the intricacies of Unicode, highlighting its evolution from a challenge of identifying text encoding to the widespread adoption of UTF-8. It also touches into key concepts such as normalization, locale dependence in rendering, and the continued relevance of UTF-16 in some systems. Some of these tips can save long-afternoons of debugging trying to figure out what is wrong with something which may seem unintuitive.
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.
Relevant upcoming MLOps conferences:
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!
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:
  • MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices.
  • 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.
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!
© 2018 The Institute for Ethical AI & Machine Learning