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The ML Engineer this week:
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If you are a Machine Learning Practitioner looking for an interesting opportunity, I'm currently hiring for a few roles including Applied Science Manager, Applied Scientist, Analytics Team Lead, and Customer Analyst - do check it out and do feel free to share broadly!
Don’t Build AI Products The Way Everyone Else Is Doing It: A great reminder on the value of defensibility in technology vs surface-level value. Key advices for production machine learning practitioners to avoid over-reliance on LLM services like ChatGPT for AI product development. Although this can be great for rapid prototyping, this can lead to a lack of differentiation, inefficiency and lack of value add. Instead, focusing on the value adding overarching use-case whilst leveraging specialised toolchains that combine fine-tuned models, custom compilers, and specialised models.
New free online course from on Vector Databases & Embeddings Applications: A great new specialized resource for machine learning practitioners, focusing on the use of vector databases in enhancing large language models. It covers the significance of embeddings in understanding data and measuring vector similarities, crucial for fields like NLP, image recognition, and semantic search. The course offers practical skills in using vector databases with LLMs, building labs for embeddings, and exploring algorithms for efficient data searches, making it ideal for professionals aiming to develop advanced data retrieval and analysis applications.
Hard to Swallow Truths They Won't Tell You About The Software Engineer Job. An insigthful and pragmatic perspective on the software engineering profession, including: 1) the gap between academic preparation and real-world demands, 2) working on existing vs new projects, 3) value creation over code perfection, 4) challenges of workplace incompetence, 5) importance of effective communication and meetings, 6) difficulties in making accurate project estimates, 7) inevitability of encountering bugs, 8) constant presence of uncertainty, 9) struggle to maintain work-life balance, and 10) crucial role of soft skills over technical skills in career advancement. This candid overview is particularly relevant for machine learning practitioners, who face similar challenges in our field.
Key takeaways from US executive order on AI: The global AI regulatory ecosystem is seeing surprisingly fast developments, recently seeing the US joining the AI regulation theme with the AI Executive order. This is a great resource summarising some of the key takeaways of the Executive Order, which broadly defines AI, encompassing a wide range of systems, and is structured around eight guiding principles focusing on safety, security, innovation, equity, and governance. NIST is also positioned to play a key role in developing AI guidelines and best practices. This order impacts organizations across all sectors, requiring a reassessment of AI use and reliance on third-party AI capabilities. There will be a growing importance in ensuring sound international interoperability; if you are interested in this space, check out our awesome AI guidelines repo which includes a broad section on policy.
The OECD adopted a new definition for AI systems: "An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment." This comes as part of the OECD Principles for responsible development and deployment of AI. This resource provides a deeper dive into some of the core themes and definitions that are being adopted, reviewed and iterated as part of this increasingly fast evolution of AI regulation and industry standards.
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.
Relevant upcoming MLOps conferences:
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!
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!
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