The MachineLearning EngineerIssue #127

This week in Issue #127:

• Get started with MLOps
• Building Drift Monitoring System
• Better Abstractions in Prod ML
• Start Guide to Anomaly Detection
• Revisiting Dev Design Patterns
• 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!

Get started with MLOps

A very comprehensive overview of the MLOps journey covering every phase of the ML lifecycle including development, testing, versioning, registries, governance, deployment, monitoring feedback, A/B testing and beyond.

Building Drift Monitoring System

An interesting end to end deep dive on a practical industry-specific usecase building a clinical data drift monitoring system in Azure with Databricks and MLOps, covering the objectives, concepts, architecture, and solution.

Better Abstractions in Prod ML

The data exchange podcast brings together a conversation with former Uber DL Leader Travis Addair, where they cover the need for better abstractions in ML. In this podcast they dive into the his work in the Horovod deep learning framework, as well as Ludwig which is the toolbox that allows users to train and test deep learning models without the need to write code.

Start Guide to Anomaly Detection

A deep dive into the diverse world of anomaly detection. In this blog post they cover a tour of techniques including Isolation Forest, Local Outlier Factor, One-Class SVM, Autoencoders, Robust Covariance Estimator and Time Series Analysis

Revisiting Dev Design Patterns

The design patterns book is a classic in computer science. The author Klaus Iglberger has put together a great video where he shares his thoughts on facts and missconceptions about some of the key architecture patterns from the book, a great resource for MLOps and ML Engineering practitioners.

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!