The MachineLearning EngineerIssue #74

This week in Issue #74:

• Coding Habits for Data Scientists
• Enterprise AI Adoption 2020
• Natural Language Processing 101
• AI Scalability & Performance
• MLOps is Not Enough
• [Updated] AI Conferences Gone Virtual in 2020
• Featured OSS Production ML Libraries
• Awesome AI Guidelines to check out this week
• + more 🚀

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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!

Coding Habits for Data Scientists

Often ML code is written in Jupyter notebooks with the main purpose of experimentation instead of scalability, which may come with undesired side-effects and may have deterimental impacts on the stability and robustness of the model beyond it’s deployment. This article has put toghether a great overview of some of the motivations as well as best practices around coding habits for data science.

Enterprise AI Adoption 2020

O’Reilly has put together and compiled the results of a survey they carried out which provides insights on AI Adoption in the Enterprise in 2020. In this article they dive into how the efforts are maturing from prototype to production on AI, and how companies are able to fill the skills gap across a broad range of industries.

Natural Language Processing 101

A fantastic tutorial that goes into the depths of Natural Language Processing. This article dives into the foundational terms and concepts in NLP, how to use the SpaCy framework for NLP, building end to end pipelines and diving into more advanced NLP concepts.

AI Scalability & Performance

The data exchange podcast goes into conversation with Wes McKinney, Director of Ursa Labs and Apache Arrow PMC member. Wes is the creator of Pandas, and author of the best selling book “Python for Data Analysis”. In this post they cover these open source projects, they dive into the need for shared infrastructure for data science, and some of the critical work at Ursa Labs.

MLOps is Not Enough

MLOps is defined as the operational complexities involved in operating production machine learning at scale. This article from microsoft provides deeper thoughts on the concept of MLOps and argues that a broader approach is required to tackle the challenge at scale - namely the Data Science Lifecycle process.

AI Conferences Gone Virtual 2020

[Updated List 17/05/2020] Due to the current global situation, a large number of conferences have had to face hard choices, several which decided going fully virtual. This hard choice has now open the doors to people from around the world to gain access to the great online content generated by expert speakers and contributors. We wanted to highlight some of these key conferences so they are not missed - these include:

• Applied AI Virtual Summit - May 21st
• Do We Need to Rethink Data for Public Go - May 29th
• Virtual Federated & Distributed ML Conf - June 18th
• Databricks Applied AI summit - June 22nd
• ICML Online - July 12th
• EuroPython 2020 - July 23

Did we miss any? Please let us know by replying to the newsletter email or by simply emailing us at a@ethical.institute

OSS: Black Box Explainability

The topic for this week’s featured production machine learning libraries is Data Visualisation. 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:

• XAI - eXplainableAI - An eXplainability toolbox for machine learning.
• Microsoft InterpretML - InterpretML is an open-source package for training interpretable models and explaining blackbox systems.
• SHAP - SHapley Additive exPlanations is a unified approach to explain the output of any machine learning model.
• ELI5 - “Explain Like I’m 5” is a Python package which helps to debug machine learning classifiers and explain their predictions.

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!

OSS: Awesome AI 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 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:

• 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

• 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 systems.

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