Well architected tool - Serverless and Machine Learning Lens
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The document discusses the AWS Well-Architected Tools for serverless and machine learning workloads. It describes the key areas and layers to consider for serverless application design, including compute, data, streaming/messaging, user identity, edge, deployment and monitoring. It then discusses how the Machine Learning Lens focuses on designing, building and implementing ML resources based on operational, security, reliability, efficiency and cost optimization pillars. Using these lenses and best practices allows for faster, lower risk ML workload development and more informed business decisions.
Well architected tool - Serverless and Machine Learning Lens
- 1. The Well-Architected Tools: Serverless Lens & Machine Learning Lens
- 3. Let’s start by listing the areas that are important in the application design proces while thinking ablout Serverless solutions. Those are: ▪ compute / computing layer ▪ data layer ▪ streaming and messaging layer ▪ user identity management layer ▪ edge layer ▪ system deployment and monitoring ▪ deployment approaches
- 4. Compute layer It is responsible for managing requests from external systems, controlling accesses and taking care of their correct authorization. Provides a runtime environment in which to deploy and execute business processes.
- 5. Data layer It is responsible for managing permanent data storage from the system level. It provides mechanisms to ensure the safe storage of information. It also provides mechanisms to trigger events (events triggering) in response to changes in data.
- 6. Streaming and messaging layer You could say that it is a communication layer. The message layer is responsible for communication between the various components of the environment. The streaming layer is responsible for managing real-time data analysis and processing.
- 7. User identity management layer It is responsible for managing the identity, authentication and authorization of the interface for both external and internal clients. AWS services useful in the data layer:
- 8. Edge layer It is responsible for presenting issues and communication with external clients. Amazon CloudFront provides a CDN that will securely store content and data from initial applications with delays and optimal transfer speeds.
- 9. System monitoring and deployment The monitoring layer is responsible for managing the system’s visibility by creating metrics and creating contextual awareness of how the system works and behaves over time. The deployment layer defines how workloads change during versioning in the management process.
- 10. System monitoring and deployment The monitoring layer is responsible for managing the system’s visibility by creating metrics and creating contextual awareness of how the system works and behaves over time. The deployment layer defines how workloads change during versioning in the management process.
- 12. Deployment approaches Source: AWS Serverless Lens Whitepaper
- 14. Machine Learning Lens focuses on the issues of how to design, build and implement resources connected with the machine learning area in the AWS cloud. Like the Well-Architected Framework, it is based on five pillars: operational, security, reliability, operational efficiency and cost optimization. Although ML Lens has been prepared to support the Well- Architected Framework, it can also be used alone. The scheme below shows the principles of the Framework and examples of verification questions used during the audit of workloads using Machine Learning Lens. Machine Learning Lens
- 15. Source: Amazon Web Services
- 16. The main components of the document are: ▪ pillars, ▪ workloads design rules, ▪ questions regarding the assessment of existing or planned workloads, ▪ best practices. How to use Machine Learning Lens?
- 17. ▪ By designing in accordance with the Well-Architected Framework, machine learning workloads can be built and implemented faster. ▪ Another plus is the reduction of technological risk (e.g. by automating deployment and the possibility of its evaluation during the design process). ▪ The use of best practices allows you to make more informed business decisions. Benefits of using Machine Learning Lens
- 18. ▪ Using “whitepapers” prepared by AWS allows you to realize how to meet even the most restrictive design or legal conditions, especially taking into account the issues of ongoing compliance with the security requirements of tools and services provided by AWS. ▪ An additional plus is a possibility of performing a free assessment of existing loads and preparing an optimized solution, based on free vouchers for AWS services, which can be earned with the participation of an authorized AWS Partner. Benefits of using Machine Learning Lens
- 19. Any questions? We can help you! Feel free to contact us kontakt@lcloud.pl www.lcloud.pl Thank you for your time! All source materials in the presentation have been appropriately marked.