Item #:
238684559
MLOps Engineering at Scale: Deploying Pytorch Models on Aws
Item #:
238684559
NZD 146
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MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors.
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Product Details
- Learn how to rapidly bring machine learning models to production with MLOps and pre-built serverless tools
- Gain knowledge in extracting, transforming, and loading datasets
- Understand the concept of automatic differentiation in PyTorch
- Deploy model training pipelines as a service endpoint
- Learn to monitor and manage the lifecycle of your ML pipeline
- Implement efficient machine learning systems using pre-built services from AWS and other cloud vendors
| Publisher | Manning Publications |
| Publication date | 1 Mar. 2022 |
| Edition | 1st |
| Language | English |
| Print length | 250 pages |
| ISBN-10 | 1617297763 |
| ISBN-13 | 978-1617297762 |
| Dimensions | 18.73 x 1.78 x 23.5 cm |
Product Description
MLOps Engineering at Scale: Deploying Pytorch Models on Aws
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Product Buying Guide
MLOps Engineering at Scale is designed to meet the needs of data scientists, machine learning engineers, and AI developers who require a robust platform for managing, automating, and monitoring their machine learning workflows. This buying guide aims to provide comprehensive information to assist potential buyers in making an informed decision.
Product Specifications
- Data Storage Capacity: 1TB-10TB
- Memory: 16GB-128GB RAM
- Processor: Intel Xeon Gold or AMD EPYC
- Operating System: Linux-based
Key Features
- Automated Model Deployment
- Customizable Workflow Automation
- Advanced Performance Monitoring
- Scalable Infrastructure
Usage Scenarios
- Large-scale Data Processing
- Continuous Integration and Deployment
- Resource-intensive Model Training
- Real-time Model Inference
Usage Scenarios
- Amazon SageMaker
- Azure Machine Learning
- Google Cloud AI Platform
Some User Review
- The platform's automation capabilities have significantly improved our team's productivity and efficiency.
- Scalability and robust performance monitoring make it ideal for handling our large-scale ML projects.
- The customizable workflow automation feature allows us to tailor the platform to our specific needs, providing great flexibility.
Competitors
- MLOps Engineering at Scale offers competitive pricing compared to other leading MLOps platforms, making it a cost-effective solution for businesses looking to scale their machine learning operations.
Buying Considerations
- Assess the specific requirements of your machine learning projects to ensure that the platform's specifications align with your needs.
- Compare the pricing and features of MLOps Engineering at Scale with its competitors to determine the best fit for your business.
- Consider the scalability and customization options offered by the platform to accommodate future growth and evolving project requirements.
Conclusion
In conclusion, MLOps Engineering at Scale provides a powerful, scalable, and cost-effective solution for managing advanced machine learning workflows. With its robust features and positive user feedback, it is a competitive choice for businesses seeking to streamline their MLOps processes.
View LessMLOps Engineering at Scale is designed to meet the needs of data scientists, machine learning engineers, and AI developers who require a robust platform for managing, automating, and monitoring their machine learning workflows. This buying guide aims to provide comprehensive information to assist potential buyers in making an informed decision. Continue Reading
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English edition Carl Osipov Format: Paperback Editorial Review
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NZD 146
Order now and get it around Saturday, November 07
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Only 4 items left in stock.
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PCI DSS compliant and ISO 27001:2022 certified, with encrypted payments and full buyer protection on every order.
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Features & Benefits
- Learn how to rapidly create flexible and scalable machine learning systems without taking on the costly overhead of physical hardware.
- Master how to deploy model training pipelines as a service endpoint, monitor and manage your pipeline’s life cycle and measure performance improvements.
- No cloud experience is required to understand and implement the concepts mentioned in the book.
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