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THE ML ENGINEER 🤖
Issue #89
 
 
This week in Issue #89:
 
 
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If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
 
 
 
The FastAI team has been creating a lot of fantastic content that covers all areas of machine learning. A great resource is their Practical Data Ethics course, they cover several very important topics around the practical implications and applications in this field, as well as it's critical importance in our day to day professional lives.
 
 
 
The data exchange podcast comes back this week with a great podcast with Rasa CTO Alan Nichol. In this podcast, Alan delves into the state of developer tools in the AI conversational space, as well as best practices for building conversational AI applications.
 
 
 
Machine learning mastery comes back with a great tutorial on computational learning theory, which refers to the mathematical frameworks for quantifying learning tasks and algorithms. In this tutorial, Jason covers how computational learning methods use formal methods to study learning tasks and algorithms, as well as a couple of hands on algorithms to dive into this field.
 
 
 
Topic modelling is a technique to extract the hidden topics from large volumes of text. Latent dirichlet allocation is a popular algorithm for topic modelling, and the Python Gensim package is a great framework to implement these techniques. This very comprehensible resource provides a full end to end introduction into the theoretical and practical applications of topic modelling.
 
 
 
Optimal character recognition has been a big challenge in industry and research, as well as a big pre-requisite in a lot of NLP real-world applications. A really interesting project called EasyOCR is bringing together an open source solution on top of Pytorch that provides deep learning based optical character recognition capabilities.
 
 
 
 
 
 
The topic for this week's featured production machine learning libraries is Model Serving Frameworks. 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. The four featured libraries this week are:
 
  • KFServing - Serverless framework to deploy machine learning models in Kubernetes with KNative
  • Seldon Core - Open source platform for deploying and monitoring models in kubernetes with rich DAG structures
  • Cortex - Cortex is an open source platform for deploying machine learning models—trained with nearly any framework—as production web services.
  • Tensorflow Serving - High-performant framework to serve Tensorflow models via grpc protocol able to handle 100k requests per second per core
 
If you know of any libraries that are not in the "Awesome MLOps" list, please do give us a heads up or feel free to add a pull request
 
 
 
 
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 thiese 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. We will be showcasingitg three resources from our list so we can check them out every week. This week's resources are:
  • 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
  • From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices
 
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 UK-based research centre that carries out world-class research into responsible machine learning.
 
 
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