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Issue #188
This #188 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 10,000+  subscribers. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions 🚀
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This week in Issue #188:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
During the last few years we have seen a growth in populary and adoption of Online Analytical Processing (OLAP) systems and databases due to the growing ubiquity of real time data. Uber showcases in this article how they have adopted the OLAP database Apache Pinot and scaled it for analytical queries on terabytes-scale data in real time.
Netflix is known for its loosely coupled and highly scalable microservice architecture to process terabytes of data to deliver their streaming services. This Netflix Engineering article dives into how they avoid exposing hundreds of microservices to UI developers by providing an univied API aggregation layer at the edge leveraging GraphQL federation.
Late in 2018, Lyft engineering completed decomposing our original PHP monolith into a collection of Python and Go microservices. This four-part series will walk through the development environments that served Lyft’s engineering team as it grew from 100 engineers and a handful of services to 1000+ engineers and hundreds of large-scale data services, including challenges, solutions and lessons learned.
Data anotation tools and synthetic data generation systems continue to become growingly key in large scale produciton machine learning systems. This article provides a bird's eye overview of the state of data annotation & synthetic data generation tools, including trends, concepts and frameworks.
Large language models such as the more recent text-to-image DALL-E model suggest a promise to advance the creative sectors. NVIDIA has released an interesting and innovative implementation of their GAN based prototype presented at SIGGRAPH as the GauGAN AI Art Tool, which now lets anyone convert simple stick-figure like drawings into impressive creative paintings.
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.
Conferences we'll be speaking at:
  • EuroSciPy - August 29th @ Basel [Industry-strength DALL-E]
Other relevant upcoming MLOps conferences:
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!
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!
© 2018 The Institute for Ethical AI & Machine Learning