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THE ML ENGINEER 🤖
Issue #48
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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!
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ONNX has joined the Linux Foundation! This is an incredibly exciting announcement given the potential this presents towards standardisation of protocols in the machine learning ecosystem. ONNX stands for Open Neural Network eXchange, and it is an open format used to represent machine learning and deep learning models, which provides for advanced and standardised functionalities for model creation and export, visualization, optimization, and acceleration capabilities.
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MLOps is a concept that is used to define the challenges and methodologies to continuously integrate, deploy and monitor machine learning in production. Open Source Engineer Ryan Dawston has put togher a great article that provides a high level overview of what MLOps is, and how machine learning is different to traditional software.
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When managing hundreds or even thousands of models in production it is necessary to introduce automation across the deployment and integration process. This great article provides a thorough overview of the nuanced challenges that machine learning introduces when deploying at scale, and provides some of the core concepts that need to be taken into consideration when introducing automation for continuous deployment of ML.
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Instacart machine learning engineer Abhay Pawar has put together a very comprehensible article on lessons learned reaching top 2% in a Kaggle competition. In the article he covers best practices to learn more from the data to be able to build feature understanding, and iteratively improve the solution to ensure reliable results.
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O'Reilly Chief Scientist Ben Lorica has announced a brand new podcast that focuses in Machine Learning called "The Data Exchange". The first episode dives right in with a conversation with Paco Nathan exploring core trends in ML including data governance, autoML, notebooks and deep learning libraries.
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The theme for this week's featured ML libraries is Industry Strength NLP. The four featured libraries this week are:
- SpaCy - Industrial-strength natural language processing library built with python and cython by the explosion.ai team.
- GNES - Generic Neural Elastic Search is a cloud-native semantic search system based on deep neural networks.
- Flair - Simple framework for state-of-the-art NLP developed by Zalando which builds directly on PyTorch.
- CTRL - A Conditional Transformer Language Model for Controllable Generation released by SalesForce
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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 showcasing three resources from our list so we can check them out every week. This week's resources are:
- Oxford's Recommendations for AI Governance - A set of recommendations from Oxford's Future of Humanity institute which focus on the infrastructure and attributes required for efficient design, development, and research around the ongoing work building & implementing AI standards.
- San Francisco City's Ethics & Algorithms Toolkit - A risk management framework for government leaders and staff who work with algorithms, providing a two part assessment process including an algorithmic assessment process, and a process to address the risks.
- ISO/IEC's Standards for Artificial Intelligence - The ISO's initiative for Artificial Intelligence standards, which include a large set of subsequent standards ranging across Big Data, AI Terminology, Machine Learning frameworks, etc.
- Linux Foundation AI Landscape - The official list of tools in the AI landscape curated by the Linux Foundation, which contains well maintained and used tools and frameworks.
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We feature conferences that have core ML tracks (primarily in Europe for now) to help our community stay up to date with great events coming up.
Technical & Scientific Conferences
- Data Natives [21/11/2019] - Data conference in Berlin, Germany.
- ODSC Europe [19/11/2019] - The Open Data Science Conference in London, UK.
Business Conferences
- Big Data LDN 2019 [13/11/2019] - Conference for strategy and tech on big data in London, UK.
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About us |
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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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© 2018 The Institute for Ethical AI & Machine Learning
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