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Issue #222
This 222 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 25,000+  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 the ML Engineer:
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Language Models ArXiv Paper
Language modeling studies probability distributions over text strings, and is used in NLP for various applications such as text generation, speech recognition, and machine translation 💡 Conventional language models (CLMs) predict linguistic sequences, while pre-trained language models (PLMs) have broader applications and are trained in a self-supervised manner. This paper provides an introduction to both CLMs and PLMs and discusses their linguistic units, structures, training and evaluation methods, applications, relationship, and future directions in the LLM era.
Discord, the instant messaging app with over 300 million active users, has shared insights into how they store trillions of messages 🤖 The article explains how they transitioned from MongoDB to Cassandra and then to ScyllaDB due to scalability requirements of trillions of messages. The case study demonstrates the challenges in managing and scaling a distributed database to store and retrieve large amounts of data for applications with significant user activity.
Prompt engineering is a new discipline that helps to develop and optimize prompts for efficient use of large language models (LLMs) in various applications and research topics ⚙️ It enables better understanding of the capabilities and limitations of LLMs and is used to improve the capacity of LLMs on common and complex tasks like question answering and arithmetic reasoning. This new prompt engineering guide contains papers, learning guides, models, lectures, references, new LLM capabilities, and more.
Open Assistant is an open-source project that aims to provide a chat-based large language model to everyone 🤗 The project is also enabling data collection to submit, rank, and label model prompts and responses to train models via crowd-sourcing and enable an open accessible AI assistant framework that can perform meaningful tasks, research information, and be personalized and extended by anyone.
A great tutorial to automate your personal finances with Airflow 🌀 This tutorial walks throug the steps to automate budget tracking and generate a dashboard for visualizing personal financial data. It involves common used tools across the ecosystem including containers, great expectations, airflow, postgres and more.
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 spoke at recently with published video:
Other 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