Subscribe to the Machine Learning Engineer Newsletter

Receive curated articles, tutorials and blog posts from experienced Machine Learning professionals.

This week we continue celebrating 40,000+ subscribers who are now part of the Machine Learning Engineer Newsletter πŸš€ It is our huge honour to celebrate this milestone together with our growing community πŸ₯³πŸΎπŸŽˆ
Issue #233
This 233 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 30,000+  subscribers. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions πŸš€
If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter,  πŸ’Ό Linkedin and  πŸ“• Facebook!
 This week in the ML Engineer:
Thank you for being part of over 35,000+ ML professionals and enthusiasts who receive weekly articles & tutorials on production ML & MLOps πŸ€– If you havent, you can join for free at ⭐
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
DeepLearning.AI has released a set of free courses on various practical generative AI areas. The courses, ranging from beginner to advanced levels, and include: Building Systems with the ChatGPT API, ChatGPT Prompt Engineering for Developers, LangChain for LLM Application Development and How Diffusion Models Work. Each course requires basic Python knowledge and are a good way to break into the practical concepts and use-cases for real-world large language models.
This paper presents an insightful use-case applying transformer-based models to forecasting problems at scale through a practical application in pricing at European fashion e-commerce company Zalando. The model uses a transformer-based architecture, and highlights the advantages of transformer-based models for forecasting when dealing with large datasets, and provide evidence of scaling laws for transformers in forecasting, which suggests that predictive performance continues to increase with the size of the training set. They also discuss challenges such as cold start problems, short history, and sparsity, intermittency, and integration of both past and future coveariates, providing examples of how these are addressed in the model.
One of the most complete and real-life-like practical and guided projects to take development skills to the next level by building a fully fledged ride sharing application and learn about distributed systems. It begins with foundational server setup and progresses through more complex aspects such as backend and frontend development, database connectivity, Docker deployments, and managing environment variables. The latter part of the blog posts focus on the specific functionalities of the app simulation, including car animation, route planning, and driver-customer matching.
LangChain is an open-source framework that uses large language models to build real-world applications for various use cases. It connects LLM models, such as OpenAI and HuggingFace Hub, to external sources and provides abstractions and tools to interface between text input and output. LangChain's key modules include Models, Prompts, Indexes, Memory, Chains, Agents, and Callbacks. This is an insightful resource that explains how to build an LLM-powered app using LangChain and Streamlit, which involves obtaining an OpenAI API key, setting up the coding environment, building the app, and deploying it​.
An interesting resource that explores the real-cost of programmer interruptions and context switching. It outlines how interruptions can take at least 10-15 minutes to recover from, with complex tasks taking longer. Context switching, or moving between different tasks, is even more mentally demanding and requires significant effort to rebuild the working state. Modern Integrated Development Environments (IDEs) can help mitigate these issues by remembering the last working state, including files, cursor positions, breakpoints, and more. Additionally, larger screen real estate can enhance productivity by allowing more code visibility, facilitating denser contexts.
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!
Β© 2018 The Institute for Ethical AI & Machine Learning