Issue #324 - The ML Engineer ??
Alejandro Saucedo
AI & Data Executive @ Zalando | Advisor @ UN, EU, ACM, etc | Join 70k+ ML Newsletter
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This week in Machine Learning:
If you're looking for an interesting career opportunity, I'm ?hiring for a few roles including Data Science Manager (Forecasting), as well as Principal Product Manager (Forecasting) - check them out and please do share with your network!
China strikes again with the new Foundation Model from Alibaba that rivals the tech giants in the West, this time with a text-to-video model; and DeepSeek-style it is open source! WanX 2.1 series have been released as an open-source video generation model from Alibaba Group supporting Multi-Modal Video Generation, chunk-wise processing and a feature cache mechanism, FSDP-based model sharding + 2D Context Parallelism and Multi-Lingual Generation. This model also shows a pretty mind-blowing design as it was trained in quite a highly efficient context, which are enabling general-purpose GPUs to support them, opening opportunitities for really interesting on-machine app opportunities!
What is happening in ML competitions? This analysis across 400+ ML competitions shows upwards trends with $22M in prize money, Kaggle growing as the leading platform, emerging libraries like Polars taking the world by storm, and Pytorch solidifying as the deep learning framework of choice: It seems that organisations are realising the opportunity and potential of AI Kaggle competitions to spark innovation, and these are also sparking insightful trends in the ML ecosystem. We are seeing that classical methods like gradient-boosted trees keep beating other methods in tabular and time series; convolutional NNs keep leading the way in vision tasks; NVIDIA GPUs (eg A100/H100) keep leading the way in compute, and; techniques like quantisation and synthetic data generation are clearly mechanisms that enable competitors take the prize often.
Chip Huyen comes back once again with a fantastic take on LLM agentic systems beyond the hype: Chip has been one of the leaders defining the conceptual framework to reason about Agentic Systems, and this keynote is yet another great resource to build further intuition on the fast-changing ecosystem of agentic systems as these are plugged in further and further into broader APIs and services. This is quite a pragmatic overview which talks about some of the best practices to consider when building agentic systems, as well as the potential - i.e. effective context management, balancing short-term and long-term memory, etc.
Carnegie Mellon University has just released an online course that teaches you step-by-step how to build your own database from scratch in C++: If you are looking to build a robust knowledge on foundational concepts in computer science, this is definitely a great resource as it teaches the key concepts indatabase management systems. CMU has put together an database from scratch that enables students to interactively build and extend the system with core concepts across the end-to-end lifecycle of database management systems. Check it out - definitely encourage diving even into the course projects as they are really practical!
We are seeing a massive surprise, with China's TikTok open sourcing a major component of their large-scale LLM agentic system stack: AIBrix is TikTok's cloud-native infrastructure to orchestrate and optimize large-scale LLM inference through traditional microservice and ML serving systems. It is quite exciting to see Chinese giants making available state-of-the-art LLMOps systems that showcase state-of-the-art learnings such as dual-plane architectures to manage dynamic model metadata, whils also handling the LoRA adapter scaling and multi-node orchestration using Kubernetes and Ray. They dive into quite a lot of details in the documentation on how they also handle key features in the dataplane with an LLM-aware gateway and distributed KV cache to efficiently handle stateful inference requests.
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 2025:
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.