Stackoverflow 2024 Dev Survey The 2024 Stack Overflow Developer Survey is out! As always a stream of great insights, this edition bringing data on GenAI adoption for develope productivity: This year's data reveals that 76% of developers are using or planning to use AI tools, though only 43% trust their accuracy and 45% believe AI struggles with complex tasks. Despite this, 70% do not see AI as a threat to their jobs, viewing it instead as a complementary tool that reduces mundane tasks and allows for focus on more strategic work. |
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Netflix's Workflow Orchestrator Netflix open sources their in-house alternative to Airflow, following Airbnb's release of Airflow as their solution to workflow management which has been adopted across the board. Netflix releases a new general-purpose, horizontally scalable workflow orchestrator designed to manage large-scale workflows like data pipelines and ML model training. This framework was designed to handle the entire workflow lifecycle, including retries and task distribution, with dynamic parameter support through a secure expression language ensuring scalability across thousands of workflow instances and half a million jobs daily. |
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META Releases LLAMA 3 Meta release Llama3 with an exciting push challenging closed/proprietary models like OpenAI: The models released include up to 405 billion parameters, and support tasks across multilinguality, coding, and reasoning. What is most impressive as well is the infrastructure innovations required to achieve this, namely bringing together 16,000 NVIDOA H100 GPUs. The training infrastructure leverages Meta's production clusters for reliability and efficiency, adopting what they refer to as "4D parallelism" for scalable computation with reduced latency. |
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Lessons from a Year of LLMs Lessons brought together by a group of experts building production LLM products throughout the last year: A great set of best practices in prompting, Retrieval-Augmented Generation, workflow optimization, and evaluation relevant for development of LLM-powered apps. Some of the key learnings include the importance of prompt engineering using structured outputs, employing deterministic workflows, and establishing robust evaluation and monitoring frameworks starting with inference APIs, avoiding unnecessary finetuning, focusing on domain-specific applications, and building LLMOps for faster iteration and product differentiation. |
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Google's Climate Foundation AI Google enters once again the forecasting foundation model race after their release of TimesFM with a new Deep Learning-based model for climate-forecast: Google is integrating ML with traditional physics-based solvers to advance weather and climate forecasting at global scale. These models are trained on ERA5 reanalysis data for up to 5-day weather forecasts and use end-to-end differentiable solvers for atmospheric dynamics. They introduce a new architecture called NeuralGCM which achieves comparable or superior accuracy to leading models like ECMWF-ENS and GraphCast for both deterministic and ensemble forecasts, offering significant computational savings. This is certainly an interesting space to watch following the release of similar models from other tech giants like Microsoft - looking forward to start seeing more accurate forecasts commodatized across the board! |
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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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