This audiobook is narrated by a digital voice.
Cloud AI Mastery: Deploying Models at Scale with AWS, Azure, and Google Cloud" is a comprehensive guide for data scientists, machine learning engineers, and developers seeking to leverage the power of cloud computing for building, training, deploying, and managing AI models at scale. The book begins by establishing a strong foundation in cloud computing principles and core machine learning concepts, including supervised, unsupervised, and reinforcement learning, as well as neural network architectures.
The core of the book dives into the AI/ML offerings of the three major cloud providers: AWS, Azure, and Google Cloud. For AWS, the book explores Amazon SageMaker in detail, covering model building, training, hyperparameter tuning, and deployment strategies like real-time inference and batch transform. It also examines AWS AI services like Rekognition, Comprehend, Translate, and Polly. The Azure section focuses on Azure Machine Learning, including workspaces, automated ML, the Designer interface, and MLOps integration with Azure DevOps. It also covers Azure Cognitive Services, exploring Vision, Speech, Language, and Decision APIs. The Google Cloud section delves into Vertex AI, covering Workbench, custom training, pre-trained models, and MLOps with Vertex AI Pipelines. It also explores Google Cloud AI APIs like the Vision, Natural Language, and Translation APIs, along with Dialogflow for conversational AI.
This audiobook is narrated by a digital voice.
Cloud AI Mastery: Deploying Models at Scale with AWS, Azure, and Google Cloud" is a comprehensive guide for data scientists, machine learning engineers, and developers seeking to leverage the power of cloud computing for building, training, deploying, and managing AI models at scale. The book begins by establishing a strong foundation in cloud computing principles and core machine learning concepts, including supervised, unsupervised, and reinforcement learning, as well as neural network architectures.
The core of the book dives into the AI/ML offerings of the three major cloud providers: AWS, Azure, and Google Cloud. For AWS, the book explores Amazon SageMaker in detail, covering model building, training, hyperparameter tuning, and deployment strategies like real-time inference and batch transform. It also examines AWS AI services like Rekognition, Comprehend, Translate, and Polly. The Azure section focuses on Azure Machine Learning, including workspaces, automated ML, the Designer interface, and MLOps integration with Azure DevOps. It also covers Azure Cognitive Services, exploring Vision, Speech, Language, and Decision APIs. The Google Cloud section delves into Vertex AI, covering Workbench, custom training, pre-trained models, and MLOps with Vertex AI Pipelines. It also explores Google Cloud AI APIs like the Vision, Natural Language, and Translation APIs, along with Dialogflow for conversational AI.
Cloud AI Mastery: Deploying Models at Scale with AWS, Azure, and Google Cloud
Cloud AI Mastery: Deploying Models at Scale with AWS, Azure, and Google Cloud
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Product Details
| BN ID: | 2940194402304 |
|---|---|
| Publisher: | Anand Vemula |
| Publication date: | 01/12/2025 |
| Edition description: | Unabridged |
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