Canva's 25B Events per Day Canva's product analytics pipeline processes 25 billion events daily, with growing use-cases across A/B testing, personalization, and insights - this is a great deep dive into how they make it happen: Canva has adopted key principles to enable massive-scale processing, such as ensuring events following a strict schema (using Protobuf), and enforcing schema with their internal service Datumgen. They collect events through a unified client, which they then ensure are validated, enriched, and routed via AWS Kinesis. |
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Uber's Kafka Tiered Storage Uber brings together their approach to supporting Tiered Storage in Kafka, which ensures decoupling between Kafka’s storage and compute resources, which enables scalable and cost-efficient data retention. Uber achieves this by introducing local storage for recent data and remote storage (e.g., S3, HDFS) for older data, which supports for optimisation of resource use and reduces operational complexity. This is quite a practical deep dive, and it is great to see the close collaboration with the open source community, with the core Kafka team also contributing closely to extend and support these use-cases. |
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DB Lessons to Know for Devs Things I Wished More Developers Knew About Databases: Often fundamentals of database design are overlooked by developers when working with databases, which can have a substantial benefit to their day to day development. Some of the key lessons include: 1) the fallibility of network reliability, 2) the varied interpretations of ACID across databases, and 3) the trade-offs between consistency, isolation, and performance. |
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Effects of Gen AI on High Skill The Effects of Generative AI on High Skilled Work: Evidence from Three Field Experiments with Software Developers. A great review on the state of GenAI bringing together three field experiments that examining the impact of GitHub Copilot on software developers' productivity. This study was conducted at Microsoft, Accenture, and an anonymous company - the study evaluates the effect of Copilot on metrics such as pull requests, commits, and successful builds (indeed not optimal metrics but still relevant insights). The results show that Copilot increases developer productivity by approximately 26%, with a particularly strong effect on the number of pull requests and builds. Junior and short-tenure developers exhibited higher adoption rates and saw the most significant productivity gains. However, the effects were not consistently statistically significant across all metrics or companies - this is obviously key, but it's great to see investments towards quantifying the causal impact of AI tools in development. |
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Explicit is Better than Implicit One of the age-old lessons on clean-code: the importance of writing explicit, clear code over implicit, ambiguous code in software development. For machine learning practitioners, this is especially relevant when working in large, collaborative codebases, as maintainability and clarity are always important. Implicit code can introduce confusion and increase the cognitive load for developers, leading to higher “WTF per Minute” (WTFPM) rates - indeed a great standardised quantifiable metric across industry (!). |
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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:
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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! |
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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!
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