Issue #296 - The ML Engineer ??
Alejandro Saucedo
Tech Executive @ Zalando | Chair/Advisor @ UN, EU, ACM, etc | Join 60k+ ML Newsletter
Thank you for being part of over 60,000+ ML professionals and enthusiasts who receive weekly articles & tutorials on Machine Learning & MLOps ?? You can join the newsletter for free at https://ethical.institute/mle.html ?
If you like the content please support the newsletter by sharing with your friends via ?? Email, ?? Twitter, ?? Linkedin and ?? Facebook!
If you are a Machine Learning Practitioner looking for an interesting opportunity, I'm currently hiring for an Applied Science Manager for Forecasting & Causal Inference for a Senior Applied Scientist for Forecasting - do check it out and do feel free to share broadly!
This week in Machine Learning:
Our talk on the main stage of WeAreDevelopers is out! This is a fresh view on the State of Production Machine Learning, highlighting key trends and opportunities for 2024: This year's edition on the State of Prod ML dives into the transition of AI models into complex data-centric systems that integrate deeply with organizational processes. We dive into the growing complexity of the machine learning ecosystem as well as how to navigate the growingly complex ecosystem of tools, ensuring robust security, advanced monitoring, and evolving roles within organizations to manage this complexity. We also dive into the impact of emerging compliance, which brings responsible AI deployment to the center.
PyCon US 2024 videos are out! As always there's a lot to catch up on, particularly for ML & Data practitioners, spanning across sessions on deep learning with PyTorch, building FPGA-based ML accelerators, AI document processing, improving ML reproducibility, advanced memory management techniques and many more. These videos are great resources for any practitioner working in the ML and data science space.
A great write-up analysing the hype in AI, challenging specifically the return-on-investment in generative AI hype. Recently we saw the Goldman Sachs report highlighting "Too Much Spend, Too Little Benefit?", with doubts on the economic viability and productivity benefits of generative AI based on costs, power demands, and limited real-world impact.
One key advice for software practitioners looking to take their career to the next level is to read research papers! More specifically, dive into foundational resources that have changed the state of the development ecosystem - and the github repo on "Papers We Love"has a massive curated collection of key computer science research papers, organized by themes. These can't be recommended enough as it contains relevant topics across machine learning, distributed systems, cryptography, and programming languages.?
Really awesome (and visually pleasing) introduction to probability and statistics. "Seeing theory" is a short course that introduces basic probability, compound probability, probability distributions, frequentist inference, bayesian inference and regression analysis. This resource came out a while back but it's still providing quite an intuitive and visual view on an important topic.
领英推荐
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.
Upcoming conferences where we're speaking:
Other upcoming MLOps conferences in 2024: ?
In case you missed our talks:
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.
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: Policy & 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:
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!
About us
The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning.
Great insights!
Ex-Infosys Springboard Intern | AI & ML Enthusiast | UI/UX Design | B.Tech CSE Student | Passionate About Innovation and Driving Change
3 个月Thanks for Sharing.