Precisely

JULY 15, 2024

Quality and formatting may differ with more autonomous domain teams producing data assets, making interoperability difficult and data quality guarantees elusive. Data discoverability and reusability. Data products must be properly designed and organized to be reused across the organization.

Data Governance 71

Data Governance DataOps Data Silos Data Pipeline 71

Precisely

SEPTEMBER 12, 2023

As a proud member of the Connect with Confluent program , we help organizations going through digital transformation and IT infrastructure modernization break down data silos and power their streaming data pipelines with trusted data. Let’s cover some additional information to know before attending.

Data Silos 52

Data Silos Apache Kafka Data Pipeline Data Quality 52

Iguazio

FEBRUARY 20, 2024

Building MLOpsPedia This demo on Github shows how to fine tune an LLM domain expert and build an ML application Read More Building Gen AI for Production The ability to successfully scale and drive adoption of a generative AI application requires a comprehensive enterprise approach. Let’s dive into the data management pipeline.

AI 52

AI AI Data Pipeline Analytics 52

ODSC - Open Data Science

SEPTEMBER 20, 2023

Tuesday is the first day of the AI Expo and Demo Hall , where you can connect with our conference partners and check out the latest developments and research from leading tech companies. Finally, get ready for some All Hallows Eve fun with Halloween Data After Dark , featuring a costume contest, candy, and more. What’s next?

Data Wrangling 52

Data Wrangling Data Science Machine Learning Machine Learning 52

IBM Journey to AI blog

JULY 27, 2023

Request a demo to see how watsonx can put AI to work There’s no AI, without IA AI is only as good as the data that informs it, and the need for the right data foundation has never been greater. Data lakehouses improve the efficiency of deploying AI and the generation of data pipelines.

AI 69

AI AI Data Scientist Data Quality 69

Snorkel AI

JANUARY 24, 2023

Data science and machine learning teams use Snorkel Flow’s programmatic labeling to intelligently capture knowledge from various sources such as previously labeled data (even when imperfect), heuristics from subject matter experts, business logic, and even the latest foundation models, then scale this knowledge to label large quantities of data.

AI 52

AI AI ML ML 52

Snorkel AI

JANUARY 24, 2023

Data science and machine learning teams use Snorkel Flow’s programmatic labeling to intelligently capture knowledge from various sources such as previously labeled data (even when imperfect), heuristics from subject matter experts, business logic, and even the latest foundation models, then scale this knowledge to label large quantities of data.

AI 52

AI AI ML ML 52

Alation

MAY 31, 2023

American Family Insurance: Governance by Design – Not as an Afterthought Who: Anil Kumar Kunden , Information Standards, Governance and Quality Specialist at AmFam Group When: Wednesday, June 7, at 2:45 PM Why attend: Learn how to automate and accelerate data pipeline creation and maintenance with data governance, AKA metadata normalization.

Data Governance 52

Data Governance DataOps Data Pipeline Business Intelligence 52

Alation

APRIL 19, 2022

Access the resources your data applications need — no more, no less. Data Pipeline Automation. Consolidate all data sources to automate pipelines for processing in a single repository. Consolidating all data across your organization builds trust in the data. The post What Is Data Modernization?

Data Governance 52

Data Governance Cloud Data Database Data Silos 52

phData

AUGUST 22, 2024

Consider a data pipeline that detects its own failures, diagnoses the issue, and recommends the fix—all automatically. This is the potential of self-healing pipelines, and this blog explores how to implement them using dbt, Snowflake Cortex , and GitHub Actions. How well does it do with our data quality issues?

SQL 52

SQL Data Quality Python Data Warehouse 52

Iguazio

FEBRUARY 8, 2024

Continuous monitoring of resources, data, and metrics. Data Pipeline - Manages and processes various data sources. ML Pipeline - Focuses on training, validation and deployment. Application Pipeline - Manages requests and data/model validations. Collecting feedback for further tuning.

ML 52

ML ML Data Scientist AI 52

The MLOps Blog

MARCH 21, 2023

An ML platform standardizes the technology stack for your data team around best practices to reduce incidental complexities with machine learning and better enable teams across projects and workflows. We ask this during product demos, user and support calls, and on our MLOps LIVE podcast. Data engineers are mostly in charge of it.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

phData

JUNE 7, 2023

You don’t need massive data sets because “data quality scales better than data size.” ” Small models with good data are better than massive models because “in the long run, the best models are the ones which can be iterated upon quickly.”

AI 59

AI AI Machine Learning Machine Learning 59

The MLOps Blog

MAY 9, 2023

The components comprise implementations of the manual workflow process you engage in for automatable steps, including: Data ingestion (extraction and versioning). Data validation (writing tests to check for data quality). Data preprocessing. This demo uses Arrikto MiniKF v20210428.0.1 Kale v0.7.0.

ML 98

ML ML Machine Learning Machine Learning 98