Snorkel AI

JANUARY 31, 2023

Valuable data, needed to train models, is often spread across the enterprise in documents, contracts, patient files, and email and chat threads and is expensive and arduous to curate and label. Inevitably concept and data drift over time cause degradation in a model’s performance.

Data Drift 52

Data Drift Explainability Data Scientist AI 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. But we chose not to go with the same in our deployment due to a couple of reasons.

ETL 52

ETL Data Drift Machine Learning ML 52

The MLOps Blog

MAY 31, 2023

The objective of an ML Platform is to automate repetitive tasks and streamline the processes starting from data preparation to model deployment and monitoring. In addition to the model weights, a model registry also stores metadata about the data and models. How to set up an ML Platform in eCommerce?

ML 59

ML Data Drift Algorithm Data Platform 59

Snorkel AI

JULY 5, 2023

I’ve been a part of projects where we’ve spent an incredible amount of money just trying to collect a small amount of data. But in other cases, as much as you can automate, the better you are. Taking advantage of weak supervision, taking advantage of synthetic data, and data augmentation, all those things can really help.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

I’ve been a part of projects where we’ve spent an incredible amount of money just trying to collect a small amount of data. But in other cases, as much as you can automate, the better you are. Taking advantage of weak supervision, taking advantage of synthetic data, and data augmentation, all those things can really help.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

I’ve been a part of projects where we’ve spent an incredible amount of money just trying to collect a small amount of data. But in other cases, as much as you can automate, the better you are. Taking advantage of weak supervision, taking advantage of synthetic data, and data augmentation, all those things can really help.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

The MLOps Blog

MARCH 28, 2023

quality attributes) and metadata enrichment (e.g., The DevOps and Automation Ops departments are under the infrastructure team. This is the phase where they would expose the MVP with automation and structured engineering code put on top of the experiments they run. “We On top of the teams, they also have departments.

Machine Learning 59

Machine Learning Automation Data Scientist ML 59

The MLOps Blog

MARCH 12, 2024

Model management Teams typically manage their models, including versioning and metadata. Monitoring Monitor model performance for data drift and model degradation, often using automated monitoring tools. Monitoring Monitor model performance for data drift and model degradation, often using automated monitoring tools.

Prompt Engineer 52

Prompt Engineer Prompt Engineering Large Language Models LLM 52

AWS Machine Learning Blog

SEPTEMBER 21, 2023

In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab , build an active learning framework on AWS to automate the processing of passenger documents. “In We used Amazon Textract to automate information extraction from specific document fields such as name and passport number.

Auto-complete 113

Auto-complete Machine Learning Computer Vision ML 113

The MLOps Blog

MAY 9, 2023

The pipelines let you orchestrate the steps of your ML workflow that can be automated. The orchestration here implies that the dependencies and data flow between the workflow steps must be completed in the proper order. Reduce the time it takes for data and models to move from the experimentation phase to the production phase.

ML 98

ML Machine Learning Metadata Automation 98