The MachineLearning EngineerIssue #121

This week in Issue #121:

• MIT Deep Learning Life Sciences
• Build Your Voice Cloning App
• Data Quality in AI Products
• Deep Learning for Cancer Dataset
• New Sklearn Features Overview
• Open Source ML Frameworks
• 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!

MIT Deep Learning Life Sciences

MIT has released as part of their OpenCourseware a fascinating course that dives into deep learning in the life sciences / computational systems biology, and contains practical knowledge on applicable and cutting edge concepts in this sub-field.

Build Your Voice Cloning App

An interesting project that contains an intuitive overview and practical repo that enables anyone to get started with Pytorch-based voice synthesis. This also provides a glimpse around the ease of access to similar state-of-the-art emerging technologies, including opportunities and challenges.

Data Quality in AI Products

The data exchange podcast dives into conversation with Superconductive CEO Abe Gong, where they dive into tools aimed at improving data quality through tools for validation and testing.

Deep Learning for Cancer Dataset

Machine Learning Mastery has put together a tutorial that walks through one of the hello-worlds of machine learning, using robust best practices for building and training a neural network on the cancer dataset.

New Sklearn Features Overview

A comprehensive blog post overview of the new features available in the latest minor 0.24 version of Sklearn, including upgrading gotchas, sequential feature selectors, individual conditional expectation plots and other new features.

OSS: GPU Accel. Frameworks

The topic for this week’s featured production machine learning libraries is GPU Acceleration 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:

• Vulkan 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 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 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:

• 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!