Generative AI succeeds or fails on the quality of your data strategy. In this hands on course, you’ll learn how to design scalable data frameworks and governance models that power LLMs, RAG, and agentic AI with reliable, ethical, and context rich information. The curriculum covers modern data strategy fundamentals, taxonomy design, and responsible AI practices—equipping you to reduce hallucinations, enforce compliance, and accelerate delivery of production ready AI solutions.
Advanced Data Techniques for Enterprise AI Systems
即将结束： 只需 199 美元（原价 399 美元）即可通过 Coursera Plus 学习新技能。立即节省
您将学到什么
Explain the foundational role of data management, security, and architecture in enterprise AI.
Implement cross-platform compatibility across AI models, datasets, and systems.
Apply vector database and embedding techniques to manage unstructured data.
Build unified data architectures using Iceberg, Delta, and DuckLake.
您将获得的技能
- Data Governance
- Generative AI Agents
- Large Language Modeling
- Data Security
- Metadata Management
- Data Strategy
- Vector Databases
- Taxonomy
- Interoperability
- Embeddings
- Zero Trust Network Access
- Data Storage
- Data Processing
- Data Store
- Machine Learning
- Retrieval-Augmented Generation
- Data Ethics
- Data Management
- Agentic systems
- Data Architecture
要了解的详细信息
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September 2025
作业
12 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 Modern Data Strategy for Enterprise Generative AI 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有6个模块
Understand the pillars of modern data strategy—management, frameworks, and governance. Learn how to secure data using encryption, access control, and ISO standards.
涵盖的内容
4个视频2篇阅读材料2个作业
Master interoperability across clouds and platforms using Docker, Kubernetes, APIs, and the SECURE framework to build scalable, resilient AI systems.
涵盖的内容
5个视频1篇阅读材料2个作业1个非评分实验室
Organize and retrieve data efficiently using metadata tagging. Learn how tagging powers Retrieval-Augmented Generation (RAG) and enhances AI accuracy.
涵盖的内容
6个视频2个作业1个非评分实验室
Explore vector databases for semantic and multimodal search. Learn about indexing strategies, ANN algorithms, and hardware acceleration for real-time AI.
涵盖的内容
8个视频4篇阅读材料2个作业1个非评分实验室
Dive into data lakes, warehouses, and lakehouses. Use tools like DuckLake and Databricks to unify structured and unstructured data for Gen AI.
涵盖的内容
8个视频2篇阅读材料2个作业1个非评分实验室
Apply governance frameworks like DAMA-DMBOK and EDM. Implement Explainable AI, Zero Trust Architecture, and Data Loss Prevention for secure, ethical AI systems.
涵盖的内容
6个视频1篇阅读材料2个作业
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常见问题
It’s a practitioner’s path to enterprise‑grade GenAI via strong data frameworks and governance—critical for reducing hallucinations, ensuring compliance, and scaling LLM/RAG systems reliably.
Professionals responsible for data platforms, AI product delivery, and governance—Data/ML Engineers, Architects, Product Managers, and Data Stewards
Design and implement a comprehensive data framework, evaluate governance against Responsible AI criteria, and operationalize RAG/LLM solutions with measurable data quality.
更多问题
提供助学金，