Machine learning is based on the storage and repetition by computers, which assist the processing and analysis of massive volumes of data and accompanying workloads, of specific patterns and behaviors. It is worthwhile to adhere to the recommendations provided in AWS’ Machine Learning Lens in order to properly regulate them.
To start, it is important to remember the Well-Architected Framework from AWS, which is a collection of guidelines and best practices for creating cloud architecture. One of our blog posts offers a description of how it helps good design. Along with the framework’s general set of guidelines, AWS has released “Lenses,” or whitepapers (docs) that focus on certain topics, such as Serverless, IoT, high performance, and machine learning problems. They support the primary framework and, as their name implies, concentrate on solutions particular to a given field.
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. It is built around the same five pillars as the Well-Architected Framework: operational, security, dependability, operational efficiency, and cost optimization. ML Lens can be used independently, however it has been designed to help the Well-Architected Framework. The framework’s guiding principles are illustrated in the diagram below, along with illustrative verification-question instances from the workload auditing with Machine Learning Lens.
How to use Machine Learning Lens?
The document’s primary elements are:
- pillars,
- guidelines for workload design,
- inquiries on the evaluation of current or anticipated workloads,
- ideal procedures.
Well-Architected Framework and Machine Learning Lens operate on a straightforward basis. There are a number of questions associated with each of the aforementioned pillars, which are mapped in the design principles and, as a result, provide a number of best practices for workloads in machine learning. Answering the questions in the document is the first step in beginning implementation efforts. It’s critical to pinpoint areas that require improvement. It is also crucial to take into account any necessary corrective measures. This enables you to develop a prioritized action plan.
In order to be able to spot potential issues early and allow time needed for their study and solution development, AWS advises using the “lens” throughout the full life cycle of machine learning workloads, especially during the design and implementation stages. The ML framework says that a key component of the designing process is safety. Utilizing authentication and permission constraints that regulate who and what can access different machine learning artifacts, workloads for machine learning should be secured in AWS.
Advantages of Machine Learning Lens
Machine learning workloads can be designed and implemented more quickly by designing in line with the Well-Architected Framework. A further benefit is the decrease in technological risk (e.g. by automating deployment and the possibility of its evaluation during the design process). You may make better business judgments by utilizing best practices. You can learn how to comply with even the most stringent design or regulatory requirements by using “whitepapers” created by AWS, especially when you consider the difficulties of ongoing compliance with the security requirements of tools and services offered by AWS. An additional benefit is the opportunity to undertake a free evaluation of current loads and prepare an optimum solution using free AWS service vouchers that can be obtained by working with an authorized AWS Partner.
For businesses, machine learning offers countless opportunities for automation, performance enhancement, and innovation. The design and execution of workloads are supported by Machine Learning Lens, which was created expressly for this purpose. As a result, when machine learning is the technology used in the project, they are not only produced in compliance with the needs of the various pillars of the AWS Well-Architected Framework, but they also indicate the direction to go in order to deliver the best solutions.
