Machine learning used to be an academic pursuit. If you wanted to work on it, you probably needed to be part of a lab or have a PhD. In early 2021, there was a shift. RiversHaveWings followed up with the VQGAN CLIP notebook. These notebooks turned text descriptions into images by guiding a GAN with CLIP.

I sometimes see people refer to neural networks as just "another tool in your machine learning toolbox". They have some pros and cons, they work here or there, and sometimes you can use them to win Kaggle competitions. Unfortunately, this interpretation completely misses the forest for the trees. Neural networks are not just another classifier, they represent the beginning of a fundamental shift in how we develop software. The "classical stack" of Software 1.0 is what we're all familiar with -- it is written in languages such as Python, C, etc.

There is a lot of excitement around AI. Recently there has been an incredible amount of buzz around the demos of models like ChatGPT and Dall-E-2. As impressive as these systems are, I think it becomes increasingly important to keep a level head, and not get carried away in a sea of excitement. The following videos/lectures are more focused on how to think about data science projects, and how to attack a problem. I've found these lectures to be highly impactful in my career and enabled me to build effective and practical solutions that fit the exact needs of the companies I've worked for.

The Bavarian State Ministry for Education and Cultural Affairs is launching the "AI@school" pilot project. Over the next five years, 15 schools are to experiment specifically with artificial intelligence in education. Andrei Karpathy, AI expert and longtime AI chief at Tesla, refers to artificial intelligence as "Software 2.0." A new generation of computer technology massively expands the capabilities of computers and thus the technological possibilities of humans. "Software (1.0) is eating the world, and now AI (Software 2.0) is eating software," Karpathy wrote in 2017.

I spent the past 5 years of my life dedicated to figuring out how to build the right software infrastructure to enable Software 2.0 development in complex applications like Autonomous Vehicles. It was an incredibly challenging, yet rewarding and humbling journey, and I thank all my amazing colleagues at NVIDIA for it. While I'm exploring what's next, and digesting this amazing journey, I wanted to share some of my learnings, and views on this, so it can maybe help some of you. First things first, if you're not familiar with what Software 2.0 is, please check out Andrej's great original write up on the topic. I introduced a similar framing then, which I used to guide our development efforts, and I will recap it here concisely.

The current series of articles on MLOps started with an analogy with DevOps. To explain how software updates happen, I am introducing the Known Unknown matrix. The so-called "known unknown" matrix is popularized by Donald Rumsfeld and divides our knowledge into four quadrants: Applied to software development, let us consider what the knowns and the unknowns for the feature team would be. As an example, let us assume that the software is an OCR program that recognizes car license plate numbers based on heuristic algorithms. In Software 1.0 (the traditional software), bugs and feature requests are two cases when software updates are needed.

The word'MLOps' became a hot keyword these days. The trend is poised to continue as AI takes more roles in the industry and society as a whole. The article is aimed at explaining what MLOps is and why the concern for data quality should be at the center of MLOps. The word MLOps is the combination of Machine Learning (ML) and Operations (MLOps ML Ops). It refers to the set of engineering practices to develop and operate machine learning models in production.

If you've ever attempted to automate business processes, reduce risk by enforcing regulatory compliance, or ensure physical safety and security in the workplace, you may have run into repetitive tasks that are expensive to scale by using a human workforce. However, when you looked into using AI, you discovered that you'd need a fast internet connection to make the system work. What if you could automatically monitor video streams in real-time? What if you could do this while keeping your video data private? What if you could do all this without even having an internet connection?

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The future of software development will be model-driven, not code-driven. Now that my 4th book ("The Economics of Data, Analytics and Digital Transformation") is in the hands of my publisher, it's time to get back to work investigating and sharing new learnings. In this blog I'll take on the subject of Software 2.0. If the animal has four legs (except when it only has 3 legs due to an accident), and if the animal has short fur (except when it is a hair dog or a chihuahua with no fur), and if the animal has medium length ears (except when the dog is a bloodhound), and if the animal has a medium length legs (except when it's a bull dog), and if… Well, you get the point. In fact, it is probably impossible to distinguish a dog from other animals coding in if-then statements.