Issue #303 - The ML Engineer 🤖
Meta's MovieGen Model, State of Prod ML 2024 Survey, Ngrok's Central Data Platform, Google NotebookLLM Podcasts, LLMs Large-Scale + more 🚀
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This week in Machine Learning:
Meta's MovieGen Model
State of Prod ML 2024 Survey
Ngrok's Central Data Platform
Google's NotebookLLM Podcasts
Open Source ML Frameworks
Awesome AI Guidelines to check out this week
+ more 🚀
META has released a mind blowing high-def AI text-to-video model that has been making waves in the AI ecosystem: This is quite an interesting release as this model encompasses also features for video editing through text prompts, personalized video creation by conditioning on user-provided images, and audio generation for sound effects and soundtracks. The space of text-to-video models is evolving quite fast - quite an exciting area to keep an eye on as we'll most likely see counter-releases from the usual suspect tech giants following this release.
Over 50% do not have any Machine Learning monitoring; important insights from 2024 Survey on The State of Production ML! We have designed the questions to provide meaningful insights on the current landscape of production ML in 2024 - if you have a chance we would be grateful if you could spend a few minutes on the survey, as you'll contribute valuable information about the machine learning tools and platforms you use in your production ML development. Your input will help create a comprehensive overview of common practices, tooling preferences, and challenges faced when deploying models to production, ultimately benefiting the entire ML community 🚀 We are also working on an interactive visualisation for everyone to be able to slice and dice across the data to derive meaningful insights on the production ML ecosystem!
A deep dive into ngrok's journey building their internal data platform with a small engineering team: An interesting case study building an internal data platform with lessons integrating data engineering into their broader software development practices, focusing on open-source tools and collaborative workflows within a Go monorepo. Ngrok transitioned from AWS services to a Kubernetes-based stack using tools like Dagster, Airbyte, Apache Flink, and dbt with a lot of interesting lessons learned along the way.
Google's introduced an Audio Overview feature to their NotebookML product which has been used to generate custom podcasts from any content provided - people have been using instruction manuals, personal notes, newsletters and anything they get their hands on. This is quite an interesting use-case of Google Gemini integrated into a Google experimental product to generate audio content for what promises to be quite a lot of high potential user-level applications.
A recent research paper from Zalando presenting a framework to leverage multimodal large language models to efficiently evaluate large-scale product retrieval systems by automating the relevance assessment of query-product pairs using both textual and visual product information. This is quite an interesting approach to reduce the time and cost associated with human annotations whilst keeping quality. This research initiative evaluates on datasets with 20,000 query-product pairs in English and German showing that MLLM-generated annotations align closely with human judgments, making it suitable for continuous, scalable, and multilingual evaluations in production environments, such as e-commerce search engines.
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:
TUM AI@WORK 10th October @ Germany
Other upcoming MLOps conferences in 2024:
ODSC West - 29th October @ USA
Data & AI Summit - 10th June @ USA
AI Summit London - 12th June @ UK
World Summit AI - 9th October @ Neatherlands
MLOps World - 8th November @ USA
In case you missed our talks:
The State of AI in 2024 - WeAreDevelopers 2024
Responsible AI Workshop Keynote - NeurIPS 2021
Practical Guide to ML Explainability - PyCon London
ML Monitoring: Outliers, Drift, XAI - PyCon Keynote
Metadata for E2E MLOps - Kubecon NA 2022
ML Performance Evaluation at Scale - KubeCon Eur 2021
Industry Strength LLMs - PyData Global 2022
ML Security Workshop Keynote - NeurIPS 2022
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!
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:
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!
About us
The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning.