Snorkel AI

JUNE 9, 2023

Full session recap The Opportunity of Data-Centric AI in Insurance Alejandro Zarate Santovena, lecturer at Columbia University and Managing Director at Marsh , asserted that AI and foundation models have a lot of potential to disrupt the insurance industry.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Viso.ai

JANUARY 30, 2024

They use automation tools like the caret package in R and Pipelines in scikit-learn. Data leakage occurs when training data is not truly representative of the population at large – source. This is a bigger deal with raw or unstructured data that engineers and developers might be using to feed the machine learning program.

Data Drift 52

Data Drift Machine Learning Computer Vision Algorithm 52

The MLOps Blog

MARCH 15, 2023

Automation : Automating as many tasks to reduce human error and increase efficiency. Collaboration : Ensuring that all teams involved in the project, including data scientists, engineers, and operations teams, are working together effectively. This includes data quality, privacy, and compliance.

ETL 52

ETL Data Drift Machine Learning ML 52

The MLOps Blog

JUNE 27, 2023

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. They should also offer version control capabilities to manage the changes and revisions of ML artifacts, ensuring reproducibility and facilitating effective teamwork.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

AWS Machine Learning Blog

MARCH 9, 2023

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. Applying these techniques allows ML practitioners to reduce the amount of data required to train an ML model.

ML Engineer 77

ML Engineer Data Scientist Convolutional Neural Networks ML 77

AWS Machine Learning Blog

APRIL 26, 2023

You may have gaps in skills and technologies, including operationalizing ML solutions, implementing ML services, and managing ML projects for rapid iterations. Ensuring data quality, governance, and security may slow down or stall ML projects. This may often be the same team as cloud engineering.

ML 88

ML Machine Learning Data Science Data Drift 88

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream.

Large Language Models 52

Large Language Models Metadata AI AI 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream.

Large Language Models 52

Large Language Models Metadata AI AI 52

The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

ETL 59

ETL ML Machine Learning Data Scientist 59

Snorkel AI

OCTOBER 12, 2023

Leveraging Data-Centric AI for Document Intelligence and PDF Extraction Extracting entities from semi-structured documents is often a challenging task, requiring complex and time-consuming manual processes. Wayfair does this by automating image tagging using a data-centric approach.

Data Scientist 64

Data Scientist AI AI ML 64

Snorkel AI

OCTOBER 12, 2023

Leveraging Data-Centric AI for Document Intelligence and PDF Extraction Extracting entities from semi-structured documents is often a challenging task, requiring complex and time-consuming manual processes. Wayfair does this by automating image tagging using a data-centric approach.

Data Scientist 59

Data Scientist AI AI ML 59

The MLOps Blog

JULY 18, 2023

It’s an automated chief of staff that automates conversational tasks. We are aiming to automate that functionality so that every worker in an organization can have access to that help, just like a CEO or someone else in the company would. How do you ensure data quality when building NLP products?

Conversational AI 52

Conversational AI Natural Language Processing AI AI 52

The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times. Data preprocessing.

ML 98

ML Machine Learning Metadata Automation 98

The MLOps Blog

JANUARY 26, 2024

Responsibility for MLOps and the ML platform was split across three teams: 1 One team focused on making tools, setting up the environment for development and training for data scientists, and helping with the productionization work. This included maintaining the underlying infrastructure and working on model deployment automation.

ML 65

ML Data Scientist Machine Learning ML Engineer 65

The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69