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.

1. The Well-Architected Tools:
Serverless Lens &
Machine Learning Lens

2. Serverless 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.

11. Deployment
approaches in
Serverless Lens

12. Deployment approaches
Source: AWS Serverless Lens Whitepaper

13. Machine Learning
Lens

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.