IBM Journey to AI blog

AUGUST 12, 2024

AIOPs refers to the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance and automate various aspects of IT operations (ITOps). However, they differ fundamentally in their purpose and level of specialization in AI and ML environments.

Big Data 106

Big Data Big Data ML ML 106

Iguazio

JANUARY 16, 2024

Sense is a talent engagement company whose platform improves the recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization including a large number of data and AI professionals.

ML 52

ML ML DataOps Data Scientist 52

DataRobot Blog

SEPTEMBER 13, 2022

Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. They develop and continuously optimize AI/ML models , collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value. Driving Innovation with AI: Getting Ahead with DataOps and MLOps.

Data Scientist 52

Data Scientist ML ML AI 52

Iguazio

JANUARY 16, 2024

Sense is a talent engagement platform that improves recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization, including a large number of data and data science professionals.

ML 52

ML ML DataOps Data Scientist 52

Precisely

SEPTEMBER 18, 2023

Trusted data is crucial, and data observability makes it possible. Data observability is a key element of data operations (DataOps). The best data observability tools incorporate artificial intelligence (AI) to identify and prioritize potential issues. Why is data observability so important?

Data Observability 52

Data Observability Data Quality Data Pipeline DataOps 52

Alation

APRIL 4, 2023

However, the race to the cloud has also created challenges for data users everywhere, including: Cloud migration is expensive, migrating sensitive data is risky, and navigating between on-prem sources is often confusing for users. To build effective data pipelines, they need context (or metadata) on every source.

DataOps 52

DataOps Data Engineering Data Engineering Data Engineer 52

BAIR

FEBRUARY 18, 2024

This is enforced with the `more` excerpt separator. --> AI caught everyone’s attention in 2023 with Large Language Models (LLMs) that can be instructed to perform general tasks, such as translation or coding, just by prompting. In this post, we analyze the trend toward compound AI systems and what it means for AI developers.

AI 40

AI AI DataOps Data Pipeline 40

Pickl AI

DECEMBER 11, 2024

Summary: Lean data management enhances agility by streamlining data processes, reducing waste, and ensuring accuracy and relevance. By leveraging AI and automation, organisations optimise operations and maintain competitive advantage in fast-changing markets. It enables faster decisions, better collaboration, and scalability.

Data Silos 52

Data Silos Data Pipeline Artificial Intelligence Artificial Intelligence 52

Precisely

DECEMBER 12, 2023

Systems and data sources are more interconnected than ever before. A broken data pipeline might bring operational systems to a halt, or it could cause executive dashboards to fail, reporting inaccurate KPIs to top management. Data observability is a foundational element of data operations (DataOps).

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52