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Data Engineer's Guide to Oracle Machine Learning and GenAI Services. Modern data engineering practices for creating efficient, AI-driven applications at enterprise scale Erik Benner, Hicham Assoudi, Tural Gulmammadov

Język publikacji: angielski
Data Engineer's Guide to Oracle Machine Learning and GenAI Services. Modern data engineering practices for creating efficient, AI-driven applications at enterprise scale Erik Benner, Hicham Assoudi, Tural Gulmammadov - okladka książki

Data Engineer's Guide to Oracle Machine Learning and GenAI Services. Modern data engineering practices for creating efficient, AI-driven applications at enterprise scale Erik Benner, Hicham Assoudi, Tural Gulmammadov - okladka książki

Autorzy:
Erik Benner, Hicham Assoudi, Tural Gulmammadov
Serie wydawnicze:
Reference Guide
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
In Data Engineer’s Guide to Oracle Machine Learning and Gen AI Services, you’ll learn how to tackle the challenges of building scalable, high-performance AI workflows in modern enterprises. Many organizations struggle to turn raw data into actionable insights while maintaining security, compliance, and operational efficiency. This book provides practical, end-to-end guidance for data engineers and architects to design, secure, implement, and optimize ML and GenAI solutions across Oracle Cloud, Oracle Database, and MySQL HeatWave.

Written by multiple Oracle experts with deep experience in Oracle technologies and enterprise data platforms, this book walks you through real-world examples and hands-on workflows—from data preparation and in-database ML to deploying GenAI-powered applications and intelligent agents. You’ll gain skills in building pipelines, managing models, leveraging vector search for advanced AI use cases, and integrating AI into business applications with APEX and Oracle Digital Assistant. Advanced topics include scalable model deployment, serverless inference, monitoring, and MLOps best practices.

By the end, you’ll be equipped to solve complex data challenges, accelerate AI adoption, and deliver measurable business impact through intelligent, production-ready solutions.

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O autorach książki

Erik Benner is the VP of Enterprise Transformation and an Oracle ACE director. He is an expert strategist for customers across the United States. His customer engagements range from enterprise cloud transformations to data center consolidation and modernization. He frequently presents at conferences such as Oracle CloudWorld, ASCEND, BLUEPRINT 4D, and FOSSY. Having worked with Oracle and Sun Systems since the mid-90s, Erik is well-versed in most of the core Oracle technologies, including Oracle Cloud, Oracle Linux, and Oracle Database. When not flying to far points of the country from the Metro Atlanta area, he enjoys spending time with his family at their observatory, where the telescopes outnumber the people.
Hicham Assoudi is an AI researcher and senior Oracle technical practitioner with long-standing experience building Oracle-based solutions and implementing enterprise Oracle applications. He holds a PhD in Computer Science specializing in artificial intelligence and natural language processing, bringing a blended academic and hands-on engineering profile. His work includes designing and implementing AI-enabled architectures on Oracle Cloud Infrastructure, including GenAI and agentic AI. He is a published author on implementing AI solutions on Oracle platforms, drawing directly from real-world system design and delivery experience.
Tural Gulmammadov is the Founder and CEO of Mergen AI, where he builds large-scale AI systems that execute regulatory workflows for food and beverage manufacturers. Previously, he served as Head of Core Generative AI development team at Oracle, leading teams of data scientists and machine learning engineers responsible for production-grade generative and applied AI systems. He is a former Instructor of AI/ML Ops at the University of Toronto in collaboration with the Vector Institute, where he taught how to design and build reliable machine learning systems. His technical interests center on the application of graph theory, discrete mathematics, and probabilistic reasoning to ML systems operating in distributed computing environments.

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