ARTICLES

Machine Learning Infrastructure vs traditional IT Infrastructure 2 years ago I had heard of DevOps and knew some bits and pieces of how AI works, such as: you take data from somewhere, train a model when you’re satisfied you feed it new data and work with the...

In this article, founder and CEO of Galileo Vikram Chatterji discusses the problems with ML data blindspots and introduces ML Data Intelligence that helps an ML team holistically understand and improve the health of the data powering ML across the organization. As a...

I’m not going to list Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, TensorFlow, PyTorch, etc. You probably know about these already. There is nothing wrong with these libraries; they’re already the bare minimum essential for data science using python. And the...

Many organizations are turning to Snowflake to store their enterprise data, as the company has expanded its ecosystem of data science and machine learning initiatives. Snowflake offers many connectors and drivers for various frameworks to get data out of their cloud...

An overview of what DevOps and MLOps have in common and what are their differences (if any). About ten years ago, the community realized that there was a barrier between the delivery and operations teams. On the one hand, there was the development team, which was...

Data drift occurs when a model sees production data that differs from its training data. If a model is asked to make a prediction based upon drifted data, the model is unlikely to achieve its reported performance. This phenomenon happens because during training, a...

We’ve all been there. You’ve spent months working on your ML model: testing various feature combinations, different model architectures, and fine-tuning the hyperparameters until finally, your model is ready! Maybe a few more optimizations to further improve the...

Is it possible to make artificial intelligence more accessible to companies, both large and small? Our latest episode is with Hyun Kim, Co-Founder and CEO of Superb AI, that is aiming to solve this challenge and building a platform that aims to make shipping AI models...

Some steps in your machine learning pipelines may need a lot of extra horsepower to finish in a reasonable amount of time. In Pachyderm, the number of pipeline workers can be increased manually using the parallelism_spec, but that still requires the underlying compute...

At Galileo, we strongly believe that the key to unlocking robust models is clean, well formed datasets. Although data quality issues are prevalent in production datasets, most modern solutions aren’t built to address these problems effectively. Rather, modern...