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Data Pipeline

DataOps

Five Important Trends in Big Data Analytics

Flipboard

FEBRUARY 3, 2023

Over the last few years, with the rapid growth of data, pipeline, AI/ML, and analytics, DataOps has become a noteworthy piece of day-to-day business New-age technologies are almost entirely running the world today. Among these technologies, big data has gained significant traction. This concept is …

Big Data Analytics

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How HR Tech Company Sense Scaled their ML Operations using Iguazio

Iguazio

JANUARY 16, 2024

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. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. Gennaro Frazzingaro, Head of AI/ML at Sense.

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AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

AUGUST 12, 2024

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

Big Data

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Webinars

Agent Tooling: Connecting AI to Your Tools, Systems & Data

Automation, Evolved: Your New Playbook for Smarter Knowledge Work

How to Modernize Manufacturing Without Losing Control

Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

MORE WEBINARS

How Sense Uses Iguazio as a Key Component of Their ML Stack

Iguazio

JANUARY 16, 2024

This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. The AI/Ml team is made up of ML engineers, data scientists and backend product engineers. With Iguazio, Sense’s data professionals can pull data, analyze it, train and run experiments.

ML ML DataOps Data Scientist

The Audience for Data Catalogs and Data Intelligence

Alation

JUNE 21, 2022

Over time, we called the “thing” a data catalog , blending the Google-style, AI/ML-based relevancy with more Yahoo-style manual curation and wikis. Thus was born the data catalog. In our early days, “people” largely meant data analysts and business analysts. ML and DataOps teams). data pipelines) to support.

DataOps

DataOps Data Scientist Data Quality Data Pipeline

Building and Scaling Gen AI Applications with Simplicity, Performance and Risk Mitigation in Mind Using Iguazio (acquired by McKinsey) and MongoDB

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. Atlas Vector Search lets you search unstructured data.

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The Shift from Models to Compound AI Systems

BAIR

FEBRUARY 17, 2024

Optimization Often in ML, maximizing the quality of a compound system requires co-optimizing the components to work well together. Operation: LLMOps and DataOps. AI applications have always required careful monitoring of both model outputs and data pipelines to run reliably.

AI AI DataOps Data Pipeline

Data Trends for 2023

Precisely

FEBRUARY 10, 2023

Advanced analytics and AI/ML continue to be hot data trends in 2023. According to a recent IDC study, “executives openly articulate the need for their organizations to be more data-driven, to be ‘data companies,’ and to increase their enterprise intelligence.”

DataOps

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What Do Data Scientists Do? A Guide to AI Maturity, Challenges, and Solutions

DataRobot Blog

SEPTEMBER 13, 2022

Data scientists drive business outcomes. 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. Download Now.

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How Data Observability Helps to Build Trusted Data

Precisely

SEPTEMBER 18, 2023

Trusted data is crucial, and data observability makes it possible. Data observability is a key element of data operations (DataOps). Read Data Observability vs. Data Quality Given they share similar aims, it might be easy to conflate the idea of data observability with data quality.

Data Observability

Data Observability Data Quality Data Pipeline DataOps

Alation 2023.1: Easing Self-Service for the Modern Data Stack with Databricks and dbt Labs

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

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What Is Data Observability and Why You Need It?

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

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