DataRobot Blog

APRIL 1, 2018

Today’s data management and analytics products have infused artificial intelligence (AI) and machine learning (ML) algorithms into their core capabilities. These modern tools will auto-profile the data, detect joins and overlaps, and offer recommendations. 2) Line of business is taking a more active role in data projects.

Analytics 40

Analytics Analytics Data Preparation Augmented Analytics 40

The MLOps Blog

DECEMBER 28, 2022

ML collaboration and timely evaluation of system design Thanks to Abhishek Rai, a data scientist with Gigaforce Inc, for collaborating with me on this interview post and reviewing it before it was published. Team composition The team comprises domain experts, data engineers, data scientists, and ML engineers.

ML 78

ML ML Data Scientist Machine Learning 78

Snorkel AI

JUNE 22, 2023

Everything that we’re seeing here is tied to statistics that we ran back in 2019 and 2020—so it’s a couple of years out of date, but I think the numbers here apply very broadly and aren’t just reflective of our own experience but are interesting to bear in mind. That’s easy and uses contextual-based anomaly approaches.

Data Quality 52

Data Quality ML ML AI 52

Snorkel AI

JUNE 22, 2023

Everything that we’re seeing here is tied to statistics that we ran back in 2019 and 2020—so it’s a couple of years out of date, but I think the numbers here apply very broadly and aren’t just reflective of our own experience but are interesting to bear in mind. That’s easy and uses contextual-based anomaly approaches.

Data Quality 52

Data Quality ML ML AI 52

Snorkel AI

JUNE 22, 2023

Everything that we’re seeing here is tied to statistics that we ran back in 2019 and 2020—so it’s a couple of years out of date, but I think the numbers here apply very broadly and aren’t just reflective of our own experience but are interesting to bear in mind. That’s easy and uses contextual-based anomaly approaches.

Data Quality 52

Data Quality ML ML AI 52

The MLOps Blog

JANUARY 18, 2023

TFT is a type of neural network architecture that is specifically designed to process sequential data, such as time series or natural language. In multi-horizon forecasting, a model is trained on data from the past to make predictions about the future. It combines the transformer architecture, which is commonly used for NLP tasks.

Deep Learning 52

Deep Learning Deep Learning ML ML 52

Snorkel AI

MAY 26, 2023

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production. The difficult part is what comes before training a model and then after.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production. The difficult part is what comes before training a model and then after.

SQL 52

SQL ML ML Python 52

Kaggle

JULY 29, 2020

David: My technical background is in ETL, data extraction, data engineering and data analytics. I spent over a decade of my career developing large-scale data pipelines to transform both structured and unstructured data into formats that can be utilized in downstream systems.

ETL 71

ETL Data Scientist Machine Learning Machine Learning 71