Machine Learning Upgrade: A Guide to MLOps, LLMs, and ML Infrastructure for Data Scientists – Kerer Kristen, Kaiser Caleb – Eksmo – 2026 – ISBN 978-5-04-201416-1
Machine Learning Upgrade: A Guide to MLOps, LLMs, and ML Infrastructure for Data Scientists
19.99 €
In stock
Machine Learning Upgrade: A Guide to MLOps, LLMs, and ML Infrastructure for Data Scientists
19.99 €
"A Guide to MLOps, LLMs, and ML Infrastructure for Data Scientists"
The era of disparate teams of analysts and developers is over. Today, large language models (LLMs) are the foundation of business. How can you harness the power of neural networks to create reliable, scalable products?
Leading industry experts present a comprehensive platform for building AI-powered applications. By tracking the creation of a real product from concept to implementation, you'll master the modern MLOps pipeline.
Included:
- From Theory to Practice: An end-to-end example of developing an LLM-based application from scratch;
- Industry Standard: Data Versioning, Experiment Tracking (MLflow), and Model Monitoring in Production;
- Responsible AI: Ethical Aspects of Algorithm Integration and Unstructured Data Management;
- Business Value: Strategies for Seamlessly Embedding LLMs into Corporate Infrastructure;
- Lifecycle: Methods for Retraining Systems on New Data Without Downtime.
This book will change the way you think about data processing pipelines. Stop thinking about machine learning as a toolbox and start building holistic systems that shape the future of your business.
The era of disparate teams of analysts and developers is over. Today, large language models (LLMs) are the foundation of business. How can you harness the power of neural networks to create reliable, scalable products?
Leading industry experts present a comprehensive platform for building AI-powered applications. By tracking the creation of a real product from concept to implementation, you'll master the modern MLOps pipeline.
Included:
- From Theory to Practice: An end-to-end example of developing an LLM-based application from scratch;
- Industry Standard: Data Versioning, Experiment Tracking (MLflow), and Model Monitoring in Production;
- Responsible AI: Ethical Aspects of Algorithm Integration and Unstructured Data Management;
- Business Value: Strategies for Seamlessly Embedding LLMs into Corporate Infrastructure;
- Lifecycle: Methods for Retraining Systems on New Data Without Downtime.
This book will change the way you think about data processing pipelines. Stop thinking about machine learning as a toolbox and start building holistic systems that shape the future of your business.
See also:
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