Sharing lessons so you don’t learn them the hard way — When I started my first job out of college, I thought I knew a fair amount about machine learning. I had done two internships at Pinterest and Khan Academy building machine learning systems. I spent my last year at Berkeley doing research in deep learning for computer vision and working…

Don’t Sleep On Data Operations When most people think about deep learning practitioners, they think of data scientists who whisper to machine learning models using special powers they learned during their PhDs. While that may be true for some organizations, the reality of most practical deep learning applications is more banal. The biggest determinant of…

There’s many ways to set up a labeled data pipeline depending on your problem setup and requirements, and here’s some lessons we’ve…

Here, we train a very simple model on the Speech Commands audio dataset and analyze its failure cases to see how best to improve it! — In the last decade, deep learning has become a popular and effective technique for learning on a variety of different data types. While most people know of applications of deep learning to images or text, deep learning can also be useful for a variety of tasks on audio data!

Machine learning on unstructured data (like images or audio) is much harder than machine learning on structured data. Fortunately, deep neural networks can produce structured representations known as “embeddings” that are remarkably effective in organizing and wrangling large sets of unstructured data. At Aquarium, we heavily utilize embeddings to make…

We’re excited to announce that Aquarium has raised $2.6M in seed funding!

Whenever an ML team discusses what they should do to improve their models, there’s inevitably a point at which someone throws up their hands and says, “Well hey, let’s just get more data and retrain the model. Maybe that will help.” There’s some promise to this idea. Holding the model…

This is the second part of our series on scaling ML teams! Read on if you’re a leader in an ML team that is starting to grow beyond 10 people and scale from one team to a collection of teams working on the ML pipeline. If you’re a smaller team…

Illustrated With Memes — So you’re doing an ML project! Maybe you want to build an object detection system for a robotics application or you want to add a recommender system to your webapp. You’ll need a team to build and improve this ML system. In the beginning, this can be a single (very…