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Issue #249
THE ML ENGINEER 🤖
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If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter, 💼 Linkedin and 📕 Facebook!
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This week in the ML Engineer:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just 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!
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MIT has a new freely available course material on efficient deep learning 💡 This course on TinyML / EfficientML focuses on enhancing the efficiency of large models for deployment on resource-limited devices, covering large models such as language and diffusion models. The curriculum delves into techniques like model compression, neural architecture search, on-device fine-tuning, and quantum machine learning. Practical components include deploying models like LLaMA 2 on laptops, with resources like lecture slides, lab assignments, and online lectures available to students and enthusiasts.
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OpenAI releases version 3 of DALL-E, the mind blowing text-to-image system now integrated with ChatGPT which enabling users to generate artwork through conversation. This version simplifies the image generation process by removing the need for prompt engineering and will be available via ChatGPT and API for certain users from October. Notably, DALL-E 3 offers enhanced image quality, ensures images are copyright-free, and has implemented safety measures against biases. OpenAI is also developing a tool to detect AI-created images which is hoped to have better luck than their discontinuied AI-generated-text-detection attempt.
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InnoQ releases an interesting report on 2023 trends in the AI, ML, and Data Engineering landscape: Insights include the rise of Generative AI, with Large Language Models like GPT-3 and GPT-4 taking center stage driven by platforms like ChatGPT. Vector databases are emerging as essential tools for enhancing search processes in LLM applications. The field is also witnessing a shift towards decentralized data engineering approaches such as Data Mesh, and a heightened emphasis on responsible and ethical AI. Meanwhile AI Coding Assistants and other GenAI-powered solutions are advancing in the technology adoption curve, indicating broader industry acceptance.
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The Complete Guide to contributing to open source celebrating Hacktoberfest 2023 🎉 Hacktoberfest 2023 celebrates open-source contributions throughout October, emphasizing the professional growth, skill development, and networking opportunities it offers. The guide highlights top repositories that can be used to get started on open source contributions. This resource also shares lessons learned in the learning journey emphasising the value of community engagement and gradual progression in open-source contributions.
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Every Programmer Should Know #1: Idempotency 💡 In the world of programming, there are many concepts that every developer should understand in order to build efficient and reliable systems. One such vital concept is idempotency, which refers to the property of an operation or function that produces the same result when applied multiple times as it does when applied only once. This may seem like a simple concept, but it has significant implications for building robust distributed systems. Grasping idempotency is vital for developers to build efficient and reliable systems.
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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.
Check out our "MLOps Curriculum" from previous conferences:
Relevant upcoming MLOps conferences:
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MLSys - 4th June @ Florida
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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!
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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.
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© 2018 The Institute for Ethical AI & Machine Learning
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The Institute for Ethical AI & Machine Learning
