IBM Journey to AI blog

OCTOBER 22, 2024

With the majority of an organization’s data being unstructured and the need to tap into this enterprise data for downstream AI use cases, such as retrieval augmented generation (RAG) cases, clients are now interested in bringing DataOps practices to unstructured data.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

phData

MAY 11, 2023

This is because databases and the data therein are constantly changing. Consider the scenario where you create a view in the database using your Development (DEV) environment. Many open-source and free tools exist, such as Flyway, Liquibase, schemachange, or DataOps. This feature can be used in many ways.

Database 52

Database SQL DataOps Data Warehouse 52

Iguazio

JULY 22, 2024

MongoDB for end-to-end AI data management MongoDB Atlas , an integrated suite of data services centered around a multi-cloud NoSQL database, enables developers to unify operational, analytical, and AI data services to streamline building AI-enriched applications. In the end, we’ll provide resources on how to get started.

AI 132

AI AI ML ML 132

Precisely

SEPTEMBER 18, 2023

Data observability is a key element of data operations (DataOps). For example, customer records should be consistent across all systems and databases, especially if they hold sensitive or personal information. Trusted data is crucial, and data observability makes it possible.

Data Observability 52

Data Observability Data Quality Data Pipeline DataOps 52

Precisely

DECEMBER 12, 2023

Data observability is a foundational element of data operations (DataOps). Schema refers to the way data is organized or defined within a database. If a new column is added to a table within your customer database, for example, it can have powerful implications for the overall health of your data.

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52

Dataversity

SEPTEMBER 15, 2021

Click to learn more about author Clayton Weir. Over the last few years, retail banking has done a tremendous job of making the user experience sleeker and more frictionless. Yet, for all of the great strides that have been made in revolutionizing the retail banking experience – both on the front- and back-end – the […].

DataOps 96

DataOps Database 96

phData

MAY 11, 2023

With the “Data Productivity Cloud” launch, Matillion has achieved a balance of simplifying source control, collaboration, and dataops by elevating Git integration to a “first-class citizen” within the framework. The admin configures the project to connect to a dedicated marketing database on Snowflake.

ETL 52

ETL Analytics Analytics Data Modeling 52

BAIR

FEBRUARY 17, 2024

DataOps: Because many AI systems involve data serving components like vector DBs, and their behavior depends on the quality of data served, any focus on operations for these systems should additionally span data pipelines. Operation: LLMOps and DataOps. for GPT-4 with 5-shot prompting or 83.7%

AI 145

AI AI DataOps Data Pipeline 145

BAIR

FEBRUARY 18, 2024

DataOps: Because many AI systems involve data serving components like vector DBs, and their behavior depends on the quality of data served, any focus on operations for these systems should additionally span data pipelines. Operation: LLMOps and DataOps. for GPT-4 with 5-shot prompting or 83.7%

AI 40

AI AI DataOps Data Pipeline 40

Dataversity

OCTOBER 22, 2021

In today’s competitive enterprise landscape, having a proper DataOps strategy in place correlates with better data intelligence and optimization within an organization – breaking down silos and enabling data democratization and better business agility at scale.

DataOps 98

DataOps Data Governance Database 98

Precisely

FEBRUARY 9, 2024

And DataOps , while not a data delivery method like a mesh or fabric, brings together people, processes, and technology across the data lifecycle to improve data quality with speed, agility, and at scale. The ultimate goal of a fabric is to bring together structured and unstructured data and make it useful for humans and machines alike.

Data Quality 69

Data Quality DataOps Data Silos Artificial Intelligence 69