Smart Data Collective

SEPTEMBER 21, 2021

A data fabric solution must be capable of optimizing code natively using preferred programming languages in the data pipeline to be easily integrated into cloud platforms such as Amazon Web Services, Azure, Google Cloud, etc. This will enable the users to seamlessly work with code while developing data pipelines.

Data Quality 131

Data Quality Data Pipeline Database Internet of Things 131

The MLOps Blog

JUNE 27, 2023

Microsoft Azure ML Platform The Azure Machine Learning platform provides a collaborative workspace that supports various programming languages and frameworks. Your data team can manage large-scale, structured, and unstructured data with high performance and durability.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Pickl AI

AUGUST 30, 2024

Assessment Evaluate the existing data quality and structure. This step involves identifying any data cleansing or transformation needed to ensure compatibility with the target system. Assessing data quality upfront can prevent issues later in the migration process.

Data Quality 52

Data Quality Data Governance Azure Database 52

Snorkel AI

OCTOBER 4, 2023

Data scientists can train large language models (LLMs) and generative AI like GPT-3.5 to generate natural language reports from tabular data that help human agents easily interpret complex data profiles on potential borrowers. Improve the accuracy of credit scoring predictions.

AI 64

AI AI ML ML 64

Snorkel AI

OCTOBER 4, 2023

Data scientists can train large language models (LLMs) and generative AI like GPT-3.5 to generate natural language reports from tabular data that help human agents easily interpret complex data profiles on potential borrowers. Improve the accuracy of credit scoring predictions.

AI 59

AI AI ML ML 59

Snorkel AI

OCTOBER 4, 2023

Data scientists can train large language models (LLMs) and generative AI like GPT-3.5 to generate natural language reports from tabular data that help human agents easily interpret complex data profiles on potential borrowers. Improve the accuracy of credit scoring predictions.

AI 52

AI AI ML ML 52

The MLOps Blog

MAY 17, 2023

Here are some specific reasons why they are important: Data Integration: Organizations can integrate data from various sources using ETL pipelines. This provides data scientists with a unified view of the data and helps them decide how the model should be trained, values for hyperparameters, etc.

ETL 59

ETL Data Pipeline ML ML 59