Issue #298 - The ML Engineer ??

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This week in Machine Learning:?

• Raschka's 3hr Coding Workshop
• Lessons on Four Years of MLOps
• 150% Productive with CursorAI
• Time Management Techniques
• Convincing PMs to Tech Debt
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more ??

Raschka's 3hr Coding Workshop

Sebastian Raschka has dropped a 3-hour workshop on building LLMs from the Ground Up! This resource comes with great timing as LLMs continue to grow in popularity, covering the foundations across input data preparation, model architecture coding, pretraining, and fine-tuning. This is quite a comprehensive resource as it dives into practical coding examples using PyTorch and LitGPT, building the core fundamentals from scratch and training simplified LLM versions like GPT-2.

Lessons on Four Years of MLOps

Lessons learned from building MLOps systems across Four Years: Key lessons from tacking production ML challenges, such as consumption forecasting where the need for robust MLOps is critical include transitioning from ML to software engineering. This encompasses key difficulty in defining the many roles and responsibilities across MLOps Engineer versus ML Engineer. As production ML systems mature, it is also challenging to juggle multiple roles as well as the pressure to keep up with rapidly evolving trends.

150% Productive with Cursor

There is an ongoing revolution on AI powered IDEs, and spending a weekend testing out the latest AI Editor "CursorAI" shows exciting potential to make developers more productive across the board: Integrating AI tools like Cursor and Claude Sonnet 3.5 into your coding workflow can significantly help launch productivity for experienced developers, and this is a great example. CursorAI is a code editor built on Visual Studio Code with native AI features that streamline the coding process - really cool to see it in action to perform automated code generation and intelligent code refactoring across multiple files. There are clear limitations - testing it in low level codebases such as C++ GPU acceleration or complex distributed computing libraries it clearly struggles to provide suggestions that are actually useful, however it is still quite exciting to see the developments in this space, definitely one to keep an eye.

Time Management Techniques

5 Time management and productivity techniques that actually work: 1) using your calendar for tasks, 2) applying the two-minute rule, 3) scheduling deep work time, 4) minimizing meetings, 5) delegating low-impact tasks. This are a great set of tips that I actually have been using myself for the last few years which I certainly recommend.

Convincing PMs to Tech Debt

The Engineer’s Guide to Convincing Your Product Manager to Prioritize Technical Debt: The key strategy involves aligning technical debt with business goals by demonstrating how it impacts developer velocity, time to market, customer experience, and long-term scalability. This is a great conceptual guide through a five-step process: 1) understanding the business strategy, 2) quantifying the problem with metrics, 3) linking technical debt to business outcomes, 4) proposing a phased solution, and 5) presenting the proposal empathetically in a format the PMs can easily understand.

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:

• TUM AI@WORK 10th October @ Germany

Other upcoming MLOps conferences in 2024:

• ODSC West - 29th October @ USA
• Data & AI Summit - 10th June @ USA
• AI Summit London - 12th June @ UK
• World Summit AI - 9th October @ Neatherlands
• MLOps World - 8th November @ USA

In case you missed our talks:

• The State of AI in 2024 - WeAreDevelopers 2024
• 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

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: Policy & 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 !

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