We recently had a chance to catch up with Heather Grebe, Senior Data Scientist at Daupler, which offers Daupler RMS, a 311 response management system, used by more than 200 cities and service organizations across North America and internationally. This platform helps utilities, public works, and other service organizations coordinate and document response efforts while reducing workload and collecting insights into response operations.
In the latest instalment of our interviews speaking to leaders throughout the world of tech, we’ve welcomed Ricardo Michel Reyes, Chief Science Officer at Erudit AI.
For our latest AI specialist interview on our blog, we’ve welcomed Adam Honig, CEO of Spiro.AI.
In this blog post, we’ll be taking a closer look at Hyper-Datasets, which are essentially a supercharged version of Clear-ML Data.
A recent VentureBeat article , “4 AI trends: It’s all about scale in 2022 (so far),” highlighted the importance of scalability. I recommend you read the entire piece, but to me the key takeaway – AI at scale isn’t magic, it’s data – is reminiscent of the 1992 presidential election, when political consultant James Carville succinctly summarized the key to winning – “it’s the economy”.
Deploying models is becoming easier every day, especially thanks to excellent tutorials like Transformers-Deploy. It talks about how to convert and optimize a Huggingface model and deploy it on the Nvidia Triton inference engine. Nvidia Triton is an exceptionally fast and solid tool and should be very high on the list when searching for ways to deploy a model. Our developers know this, of course, so ClearML Serving uses Nvidia Triton on the backend if a model needs GPU acceleration.
Principal Component Analysis (PCA) is one of the most commonly used unsupervised machine learning algorithms across a variety of applications: exploratory data analysis, dimensionality reduction, information compression, data de-noising, and plenty more. In this blog, we will go step-by-step and cover: Before we delve into its inner workings, let’s first get a better understanding of PCA. Imagine we have a 2-dimensional dataset.
It has been months ago when Toloka and ClearML met together to create this joint project. Our goal was to showcase to other ML practitioners how to first gather data and then version and manage data before it is fed to an ML model. We believe that following those best practices will help others build better and more robust AI solutions. If you are curious, have a look at the project we have created together.
