THE ML ENGINEER — WEEKLY NEWSLETTER
The MachineLearning EngineerIssue #266
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Issue #266 🤖 - MLL Forecasting & Causal Inference, Value of Open Source Software, TextToSpeech Inverting Whisper, ISO Global Standards on AI, Meta Large Scale Infrastructure + more 🚀
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This week in Machine Learning:
- Forecasting & Causal Inference
- Value of Open Source Software
- TextToSpeech Inverting Whisper
- ISO Global Standards on AI
- Meta Large Scale Infrastructure
- Upcoming MLOps Events
- Open Source ML Frameworks
- Awesome AI Guidelines to check out this week
- + more 🚀
MLL Forecasting & Causal Inference
Causal inference in forecasting can unlock actionable insights for practical applications; this research paper presents a case study applying causal forecasting in e-commerce with transformer models and double-ML: This insightful paper on “Causal Forecasting for Pricing” introduces a novel method is introduced for demand forecasting in retail pricing, emphasizing the causal relationship between price and demand. This approach combines Double Machine Learning with advanced transformer-based models to enhance pricing decisions by accurately predicting and understanding the impact of price changes on demand. The methodology stands out for its superior performance in estimating causal effects in controlled synthetic environments and real-world off-policy settings, while also being competitive in on-policy scenarios.
Value of Open Source Software
The $4.15 billion quantified value of Open Source Software analysis: A great paper that quantifies the economic impact of Open Source Software by evaluating both its supply-side value ($4.15 billion) and its significantly larger demand-side value ($8.8 trillion). The study leverages global data to assess the costs firms would incur if they had to internally develop software in the absence of OSS, revealing that firms would spend 3.5 times more on software without OSS. A notable finding is that a small group of developers (5%) contributes to 96% of OSS’s demand-side value, with the top six programming languages accounting for 84% of this value.
TextToSpeech Inverting Whisper
Inverting OpenAI’s Whisper model to create an impressive text-to-spech ML model that is open source and free to use for day-to-day tasks: WhisperSpeech is an open-source text-to-speech system that inverts the OpenAI Whisper model for safe commercial use with licensed text-to-speech recordings. Key features include optimized performance with over 12x real-time processing speed, multilingual support, and voice cloning capabilities. It is available on HuggingFace, and supported by Collabora, LAION, and the Jülich Supercomputing Centre, making it a promising tool for production machine learning practitioners in text-to-speech applications.
ISO Global Standards on AI
The first ISO standard on AI - the ISO/IEC 42001:2023(en) provides guidelines for organizations on establishing, implementing, maintaining, and continually improving an AI management system: This standard focuses on the unique characteristics and challenges of AI, such as continuous learning, transparency, and explainability, and emphasizes a risk-based approach tailored to different AI use cases and products. The standard is designed to integrate with an organization’s existing management structures and processes, addressing issues like security, fairness, and data quality. It is applicable to any organization using or providing AI-based services or products, regardless of size or nature, and aligns with other management system standards to ensure consistency and comprehensive coverage of AI-specific considerations.
Meta Large Scale Infrastructure
Meta’s Threads app achieved rapid success, garnering over 100 million sign-ups in five days, this post provides a great insights on the infrastructure behind the success: Threads was developed in just five months with minimal lead time, and leveraged key components such as the a scalable distributed key/value datastore “ZippyDB”, and a serverless-functions platform Async. These tools enabled seamless scaling and efficient handling of massive user influx and operations, showcasing the importance of quick adaptation and deployment.
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 MLOps conferences:
- FOSDEM (AI & HPC) - 3rd February @ Belgium
- State of the Open (Data) - 6th February @ UK
- World AI Cannes - 8th February @ France
- NVIDIA GTC - 17th March @ USA
- KubeCon Europe (AI) - 18th March @ France
- MLConf - 28th March @ USA
- PyData & PyCon DE - 17th April @ Germany
- MLSys - 13th May @ USA
- ODSC West - 29th October @ USA
- Data & AI Summit - 10th June @ USA
- AI Summit London - 12th June @ UK
- World Summit AI - 9th October @ Neatherlands
In case you missed our key talks:
- 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.