When architecting solutions, it is important to follow a set of rules that not only ensure the architecture meets business objectives but also meets the specific demands of projects. Here is a consolidated list of the rules proposed by Philippe PAIOLA. At the end, I am adding three more rules concerning the projects I have been working on.

𝐀𝐥𝐢𝐠𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬: The architecture must support the company’s goals, enabling teams to meet customer needs effectively. This includes scalability for workload increases and ensuring services are available 24/7.

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 𝐋𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬: Be aware of the limitations inherent in the services being used, such as execution time limits or resource caps, as these can significantly impact the architecture’s design and functionality.

𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝐄𝐟𝐟𝐨𝐫𝐭: Consider the maintenance required for different hosting models (IaaS, PaaS, SaaS), as this affects the overall effort needed to keep the infrastructure up and running smoothly.

𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: Prioritize security by using both cloud provider tools and custom solutions to protect infrastructure. Despite the costs, the importance of security cannot be overstated.

𝐂𝐨𝐬𝐭 𝐨𝐟 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: Evaluate the cost of the architecture before deployment to ensure it does not outweigh the benefits it brings to the company and its customers.

These are my three additional rules based on my experience

𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐟𝐨𝐫 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: For machine learning projects, it’s essential to have an architecture that supports quick adaptations and experimentation, allowing data science teams to innovate and refine models efficiently.

𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: Implement processes for data collection, processing, and analysis that ensure high data quality. This includes creating reliable data pipelines, cleaning and deduplicating data, and enforcing strict security policies.

𝐄𝐧𝐬𝐮𝐫𝐢𝐧𝐠 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 𝐓𝐞𝐚𝐦𝐬: Foster collaboration among data scientists, data engineers, developers, and operational teams. Utilize CI/CD workflows and platforms that offer shared spaces for model validation to streamline these collaborative efforts.

From my point of view, integrating these rules provides a comprehensive framework that addresses both general architectural considerations and the specific needs of machine learning projects, ensuring that the architecture is not only efficient and cost-effective but also flexible and secure, facilitating innovation and collaboration.

Reference

Paiola, P. (2023, November 7). Mes 5 règles d’or de l’Architecte Cloud [Video]. YouTube. https://www.youtube.com/watch?v=Uy5l3qHpXNw