The MachineLearning EngineerIssue #250

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The ML Engineer this week:

• GPT-4V(ision) First Impressions
• Causality for Machine Learning
• Hardest Part of Building Software
• Habits of Effective Engineers
• CMU Deep Learning Systems
• Upcoming MLOps Events
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more 🚀

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GPT-4V(ision) First Impressions

OpenAI’s new version of GPT-4V is straight out of a scifi movie introducing computer-vision capabilities: GPT-4V(ision) is capable of processing both text and image inputs. This article does a great job at exploring GPT-4V’s capabilities, revealing its proficiency in visual question answering, optical character recognition, and math problem-solving from images. There are interesting insights on the model limitations such as in object detection, CAPTCHA interpretation, and certain games like crosswords and sudokus. GPT-4V represents a significant advancement in machine learning, so it’s essential to be aware of its potential and constraints, especially in considering production and real world scenarios.

Causality for Machine Learning

Machine learning has made significant strides, but as its applications expand into decision-making, the importance of understanding causality becomes evident - this resource provides a comprehensive overview on Causality. Causality, as opposed to mere correlation, offers deeper insights into “why” something happens, allowing for better reasoning during interventions. The integration of causal inference with ML, championed by researchers like Judea Pearl, is an emerging field that promises to enhance the robustness, adaptability, and fairness of ML systems, moving beyond just predictions to understanding the underlying causal relationships.

Hardest Part of Building Software

StackOverflow on how the hardest part of building software is not coding, it’s requirements: Drawing parallels between AI’s proficiency in tasks with finite parameters like chess and its struggles in complex tasks like self-driving cars, the author emphasizes that software development’s intricacies resemble the latter. While AI can produce code, it can’t inherently grasp or set nuanced software requirements. The transition from waterfall to agile methodologies is highlighted, with the assertion that AI might be efficient in rewriting existing software but can’t replace the human element essential for determining software requirements.

Habits of Effective Engineers

7 Habits of Highly Effective Software Engineers: 1) prioritize prototyping to test ideas, 2) refine their effort estimation skills, 3) conduct prompt code reviews, 4) maintain comprehensive documentation, 5) engage in open technical discussions, 6) focus on task completion, and 7) exhibit an innate curiosity about new technologies and broader contexts. Great article which highlights habits that underscore the significance of hands-on engagement, clear communication, and continuous learning in software and ML development.

CMU Deep Learning Systems

The Deep Learning Systems course from CMU is now available on YouTube offering a comprehensive materian on the intricacies of deep learning. The course blends theoretical insights with hands-on implementation sessions, covering topics from foundational ML concepts to advanced topics like transformers, hardware acceleration, and model deployment. It’s tailored for machine learning practitioners aiming to bolster their expertise in deep learning systems, punctuated by student project showcases and interactive Q&A sessions.

Upcoming MLOps Events

The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below.

Check out our “MLOps Curriculum” from previous conferences:

• Responsible AI Workshop Keynote - NeurIPS 2021
• Practical Guide to ML Explainability - PyCon London
• ML Monitoring: Outliers, Drift, XAI - PyCon Keynote
• Metadata for E2E MLOps - Kubecon NA 2022
• ML Performance Evaluation at Scale - KubeCon Eur 2021
• Industry Strength LLMs - PyData Global 2022
• ML Security Workshop Keynote - NeurIPS 2022

Relevant upcoming MLOps conferences:

• MLSys - 4th June @ Florida
• Women+ in Data AND AI - 30th June @ Berlin
• EuroPython - 17th July @ Prague
• EuroSciPy - 14th Aug @ Basel
• MLOps World - 25th October @ Austin
• ODSC West - 31st October @ California
• KubeCon NA (AI Track) - 6th November @ Chicago
• The AI Summit - 6th December @ New York

Open Source MLOps Tools

Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ⭐ github stars. 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. Four featured libraries in the GPU acceleration space are outlined below.

• 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 open source and open community events that are not listed do give us a heads up so we can add them!

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:

• MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices.
• 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.

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!