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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Impedance mismatch between data scientists, data engineers and production engineers.

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Unlocking the Power of AI with a Real-Time Data Strategy

CIO

Titanium Intelligent Solutions, a global SaaS IoT organization, even saved one customer over 15% in energy costs across 50 distribution centers , thanks in large part to AI. AI is the perception, synthesis, and inference of information by machines, to accomplish tasks that historically have required human intelligence.

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P&G turns to AI to create digital manufacturing of the future

CIO

Cretella says P&G will make manufacturing smarter by enabling scalable predictive quality, predictive maintenance, controlled release, touchless operations, and manufacturing sustainability optimization. These things have not been done at this scale in the manufacturing space to date, he says. Smart manufacturing at scale.

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10 most difficult-to-fill IT roles — and how to address the gap

CIO

CIO.com’s 2023 State of the CIO survey recently zeroed in on the technology roles that IT leaders find the most difficult to fill, with cybersecurity, data science and analytics, and AI topping the list. S&P Global also needs complementary skills in software architecture, multicloud, and data engineering to achieve its AI aims. “It

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Smart Factories: Artificial Intelligence and Automation for Reduced OPEX in Manufacturing

DataRobot

This “revolution” stems from breakthrough advancements in artificial intelligence, robotics, and the Internet of Things (IoT). Python is unarguably the most broadly used programming language throughout the data science community. High-level example of a common machine learning lifecycle.

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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

Altexsoft

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Watch our video explaining how data engineering works.

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Data Lake Engineering Services

Mobilunity

Key zones of an Enterprise Data Lake Architecture typically include ingestion zone, storage zone, processing zone, analytics zone, and governance zone. Ingestion zone is where data is collected from various sources and ingested into the data lake. Storage zone is where the raw data is stored in its original format.