ML Competitions in 2023 Analysis of Machine Learning Competitions in 2023: This past year has shown a significantly higher participation, with prize pools surpassing $7.8 million, with diverse challenges across platforms like Kaggle, AIcrowd, and Hugging Face. Looking at some of the key insights from 2023, Python continues to dominate (as expected), with clear bias towards PyTorch for deep learning, as well as an emergence on LLMs (although mostly for sinthetic data generation). A lot of great insights in this in-depth analysis, with an interesting glimps on what is yet to come in 2024! |
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How Discord Stores Trillion Msgs Discord has to handle massive-scale data across their system, making it a great case study for best practices on storing trillions of datapoints effectively: Discord shares their transition from MongoDB, to Cassandra, and finally to ScyllaDB for storing trillions of messages due to scalability and performance issues & requirements. Their journey showcases their learnings and best practices, with the end restult enabling them to enhance overall performance and latency, successfully managing trillions of messages and high-traffic events like the World Cup without substantial issues. |
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5 Lessons in 6y the Hard Way 5 Lessons in 6 Years as a Software Engineer - The Hard Way 🔥 These are 5 great lessons for any software practitioner looking to take their craft to the next level: 1) Proposing solutions over problems, 2) collaboration over pristine code, 3) prioritizing team success over individual tasks, 4) the necessity of adapting to different managerial styles, and 5) the power of building trust-based relationships for genuine influence. These lessons serve as a practical guide for navigating the complexities of teamwork and leadership in the fast-paced field of machine learning. |
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Neural Networks Zero to Hero Neural Networks from Zero to Hero by Andrej Karpathy: This is a great in-depth course aimed at production machine learning practitioners with a solid Python and basic math background. It offers a thorough walkthrough from the basics of neural networks and backpropagation to constructing advanced models like GPT, with a focus on language models. The course covers essential deep learning concepts, including training principles, language modeling, Batch Normalization, and the intricacies of backpropagation, through hands-on coding and practical examples. |
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70B Param Model at Home You can now train a 70b language model at home: Check out this interesting OSS framework capable of training a 70 billion parameter language model on desktops with just two gaming GPUs (e.g., RTX 3090 or 4090). This framework leverages methodologies such as Fully Sharded Data Parallel and Quantization and Low-Rank Adaptation (aka QLoRA). It's great to see a lowering of the barrier-to-entry for training large-scale AI models, making it feasible and cost-effective for individuals and small labs to undertake projects that were previously the domain of well-funded organizations with access to expensive data center hardware. |
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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. Upcoming conferences where we're speaking:
Other upcoming MLOps conferences in 2024:
In case you missed our talks:
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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! |
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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 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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