This is an edited transcript of the keynote I gave at the Applied Machine Learning Conference in Charlottesville, VA in April 2026.I first wrote a draft of this talk by hand. This part took 2 months.I then recorded myself giving a version of this talk with MacWhisper, and transcribed it with Whisper locally. This part took 45 minutes (the total time of my practice run.)Then, I ran it through Gemini Flash 2.5 running in Pi to break into paragraphs. I also had Gemini break up my slide deck from a PDF I generated from Google Slides, into individual images to insert into the blog post and optimize the image format to webp for blog rendering. This part took about 10 minutes.Then I went through the text paragraph by paragraph manually to make corrections, remove redundant phrasing and pauses, and added clarifications to make it more legible than a talk. This part took 3 hours.Before all that, I generated the content for this talk. This part took 13 years.I’m Vicki, and I build machine learning systems.I debated for a long time how to introduce myself. Am I a data scientist? Am I still a machine learning engineer? Am I an AI engineer now? I’m not really sure. I think, like a lot of people over the past six months in the industry, I’ve been having existential angst. So, I’ll go with “I build machine learning systems.”I’ve built and broken systems at Tumblr, at Automattic, at Duo, at Mozilla.ai. Now, I build realtime personalization and search systems at Malachyte.We’ve had a lot of different conversations in all of the corners of the internet about how we should incorporate LLMs into machine learning workflows. And the question that came to me is, if all we’re doing is generative AI, where does traditional machine learning even fit in here? Are we still doing machine learning at all? So, the larger question behind my existential angst is, what is the state of machine learning engineering as an industry today? That is - is it still worth doing machine learning engineering?And the second question that came to me was, not only is it still worth doing ML, but, in an era where we’re having LLMs generate a lot of code, when the most important thing is for us to ship quickly, to get to a prototype quickly, is it still worth doing machine learning well?So hopefully we’ll be able to definitively solve all of my existential crises in 45 minutes, but until then, I wanted to take a step back and talk about flowers.In 2024, the lovely folks at PyData Amsterdam, invited me to give a keynote. I called it Build and Keep Your Context Window. ChatGPT was still fairly new, out for just about a year and a half at the time, and everyone was freaking out. I talked about how important it is to build and keep your own context window - the historical understanding of software tools and concepts, so that we can use the new tools and understand where they fit into the context of existing software engineering.The analogy I used was the ChatGPT UI. At the time, everybody was using the text interface, where you have a sidebar and you have the big text box. All your chat sessions live in the sidebar, and the sessions have titles. And I talked about that as being part of a context window.The context window is the amount of text that a model can recall at any given time, as measured in tokens. If the text you input overflows the context window, the model loses the thread of the conversation and can’t reply coherently anymore. What determines the context window in reply is the attention mechanism in the Transformers model. And the attention mechanism is really a cache, which is really a hash map. So if you understand all these four things and how they fit together as concepts that naturally emerge over time successively, it’s not perhaps as surprising that we get the emergence of LLMs.Before I gave this talk, I got to spend a few days in Amsterdam (I was told they were the only two sunny days in the Netherlands in September) and particularly in the Rijksmuseum, which is the national Dutch museum of art.It’s absolutely enormous and beautiful. You could spend days there. But I only had a few hours, so of course I went to see the headline piece, which is the Night Watch by Rembrandt. When I went to see it, it was covered by this big plastic window and there was machinery set up all around it. I was thinking, what is this? Why is this blocking the painting?When I went later to PyData Amsterdam, Robert Erdmann, who was working at the Rijksmuseum, gave an incredible talk about how he was using deep learning-based ink-removal, especially 3D imaging, pixel detection, and high resolution photography to see details in art at the museum that nobody was able to see before.So for example, for The Night Watch, they set up all this equipment to be able to photograph it many times in many resolutions. And as a result, they were able to get high resolution pictures like the figure on the left, who is believed to be actually Rembrandt, the artist, in a cameo. And if you really zoom in, further, further, you can see in his eye there’s a little white sliver.And so the question is, how do you become good enough that you know if you put just a little bit of a white dot, it will render as an iris of an eye to somebody? And so those are the kinds of questions, around the mystery of mastery, that he was now able to explore as a result of this photography.Rachel RuyschOne of the artists whose paintings are also on display at the Rijksmuseum is Rachel Ruysch, one of the premier flower painters during the Dutch Golden Age, and that’s who I really want to talk about. Being a premier painter of the Dutch Golden Age was a huge accomplishment, and I want to understand more about how she works, because maybe we can use her as a guide to navigate this crazy time where we’re doing a lot of code generation with LLMs.So who was she? Rachel lived in Amsterdam, and all she did was paint flowers. Lots and lots of different compositions of flowers in all sorts of shapes and sizes, starting from when she was 15, well into her 80s.Let’s take a step back and talk about the Dutch Golden Age for a minute. What made this era of creativity possible? In the late 1500s, the Dutch won independence from the Spanish government. The political stability was followed by economic stability and the rise of the Dutch empire. As Dutch life got significantly easier and more people joined the growing middle class, they wanted to buy art. In response, art, became seen as an important aspect of the Dutch national character, and art which had previously been mostly religious in theme, became secular. People started painting scenes from real life, science, and nature. All of this resulted in millions of paintings, many of them scientific or representative of everyday life.What people wanted to see were pictures of everyday life that were accurate, that were true to what was going on around them, of local scenes, of scientific discovery. Of themes of religion and mortality, but conveyed in a different way.So they wanted to paint stuff like gentlemen smoking and playing backgammon in a tavern. They wanted to paint stuff like people skating in the winter.They wanted to paint stuff like people getting rowdy in their house while the mistress was asleep.And if you look at all these paintings, you can see something come through, which is that the colors are down to earth. They’re very normcore.Another thing people wanted was a lot of different paintings of flowers, hundreds and thousands of paintings of flowers.Out of the five to ten million paintings that we think were created during the Dutch Golden Age, thousands of them were flowers. Why flowers? People wanted flowers in their homes that wouldn’t wilt over the long Dutch winters, and that showcased how worldly and interesting the homeowners were. Exotic and unusual flowers in particular (such as tulips of the famous Dutch tulip mania) were prized, and flower painters sought to accommodate this trend. There were many very famous Dutch flower painters and hundreds to thousands more, all producing and focusing on flowers. For artists, painting flowers accurately and lifelike was an important test of mastery.And so Rachel Ruysch came into her skills at a time when people demanded this. And she was really good at it. And so the question I had, was that, if you have all these people painting flowers, why is it important to paint good flowers? Why do you want to stand out?And the same question of, in a world where it’s easy and fast to write code, why is technical excellence still important?As I was researching this, I came across a report from NASANASA has been on my mind lately with the Artemis II mission and their orbit of the moon and their triumphant return home. Because spaceflight is so hard and so enormously error-prone NASA has been continuously on a loop to improve quality, because it’s already very dangerous to be an astronaut at NASA. I’d seen somewhere that the Artemis crew knew they had a 1 in 30 chance of dying.But NASA is always looking at risk, and they did an investigation in 2012 and came out with this report of what happens when things fail. Why do they fail? And they came up with these five problems. And the one that really struck out to me was the first one, shifting engineering excellence to insight and oversight.So there were periods when NASA outsourced a lot of their engineering to subcontractors. And what happened was that the engineers who understood the design and the system design based on actual experience left. And the shift that resulted eliminated independent analysis and testing and understanding of the systems. And this was what led to degradation of engineering quality.And if it sounds familiar, this is because it’s a very easy analogy for what happens when we outsource our work as engineers and data scientists and machine learning engineers entirely to AI.So, striving for technical excellence in industry saves us from engineering, product, and cost problems. Quality is important.The second reason is that software engineering is a craft, just like painting.And it takes time to get good at painting. It takes time to understand where to put the little white iris. It takes time to get good at painting flowers. And so it’s important to understand that because it takes time to get to quality.And finally, technical excellence is important because striving for mastery is just about what it means to be human.We like when people do things well. We appreciate people who are competent. We like it when systems are designed and built well. It feels good for us to be part of systems that work. One tangential idea that I’ve seen going around the internet is that you shouldn’t talk down to LLMs, or call them “stupid”. Not because they understand, they’re just models, but because it’s bad for us as humans. In that same way, it’s important for us to strive for good things as humans.Okay, so if we want to paint good flowers, how do we become good at painting good flowers? What made Rachel technically excellent? What might we be able to replicate?The first one is that she was in an artistic family. So she was in one of the premier families of Amsterdam where all her great uncles were painters. Her grandfather was involved in art. Her father was a great artist and botanist, Frederick Ruysch. You can see him here with this creepy skull. He collected a lot of really creepy dead things, which is where she learned about scientific accuracy, embalming, preserving flowers, etc.It was here that she also learned discipline: how to capture minute details, She was known for her technical skill, and for gathering flowers in botanic gardens and pressing them so she could have different compositions for different seasons, surprising the Dutch, who were generally orderly and used to seeing only seasonal bouquets. During her life, her paintings sold for 3x Rembrandt’s.Then when she was 15, she was apprenticed to Willem van Aelst, who was one of the premier painters in Amsterdam at the time. It was tradition at the time for masters to take on apprentices who would live with them and start with stuff like washing their paintbrushes and work all the way up to painting, and then finally produce a work of art which, if the master accepted, the apprentice could become a master in their own right. So she had a very big support system and she was steeped in a culture of mentorship being extremely important.Second, she painted her whole life. She was keenly interested in botany, in science, and made sure that her art reflected the true appearance of nature. She spent a long time - over 60 years, honing and working on her career, not even slowing down to have ten children. For her, her career was a vocation. She got inspiration from many different places, and she would do the same thing, again and again, but differently.And finally, she remixed and experimented. So you can see in the background of this particular painting, there’s a cactus. And it’s not easy to get a cactus in Amsterdam in the summer or the winter. So she got this from the New World because she had connections to Amsterdam botanical societies and she knew people who were coming and going all the time. She bartered for and preserved exotic flowers. And she also remixed flowers from different seasons by drying them. She collected them all the time. She was always changing something about how she was working.So, given all of this, how can we, as engineers, be like Rachel?Building RijksearchI thought about all this and decided to see how I could apply these ideas to a concrete side project that I was working on. And the side project is called Rijksearch, a semantic search engine.A semantic search engine searches by intent rather than keyword. So what you do is you type in “chill dude” and it’ll return you this portrait of, for example, a man with a dancing dog. Which I think is actually a really good example result. Or like a “confidential chat” or “man smoking pipe”, the last one for sure being a chill dude. So it’s a search engine that works by intent.Or, you type in “pensive” and it will return a philosopher in his study, which is definitely pensive, a boy with a golf club probably not, you don’t want toddlers to start thinking too hard about what they can do with a golf club. But like, ok, these results are not bad!Or “flower party”, which didn’t work that well because you can see it’s doing more keyword matching for “flower” than semantic matching for “party”, but this is where the craft of tuning semantic search comes into play.There are already a lot of wonderful tools that the Rijksmuseum itself has that you can build with on public data and that they’ve built with like Art Explorer.But I decided to build my own because I wanted to remix concepts. Rachel learned how to remix as an apprentice and from the artists she talked to.