| Description |
1 online resource (1 volume) : illustrations |
| Bibliography |
Includes bibliographical references. |
| Summary |
If you've begun to deploy large-scale data systems into production, or have at least explored the process, this practical ebook shows business team leaders, business analysts, and technical developers how to make your big data analytics, machine learning, and AI initiatives production ready. Authors Ted Dunning and Ellen Friedman provide a non-technical guide to best practices for a process that can be quite challenging. Rather than provide a complex review of tools, this ebook explores fundamental ideas on how to make your analytics production easier and more effective, based on the authors' observations across a wide range of industries. Whether your organization is just getting started or already has data-driven applications in production, you'll find helpful content that will help you succeed. Gain an understanding of the goals, challenges, and potential pitfalls of deploying analytics and AI to production Learn the best way to design, plan, and execute large data systems in production Focus on the special case of machine learning and AI in production Examine MapR, a data platform with the technical capabilities to support emerging trends for large-scale data Explore a range of design patterns that work well for production customers across various sectors Get best practices for avoiding various gotchas as you move to production. |
| Subject |
Real-time data processing.
|
|
Electronic data processing -- Distributed processing.
|
|
Data mining.
|
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Temps réel (Informatique) |
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Traitement réparti. |
|
Exploration de données (Informatique) |
|
Data mining |
|
Electronic data processing -- Distributed processing |
|
Real-time data processing |
| Added Author |
Friedman, Ellen, author.
|
| Added Title |
Artificial intelligence and analytics in production |
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