From a software engineering perspective, Machine Learning (ML) systems are perceived as an intricate combination of tools and components, rather than simply a collection of algorithms that predict outcomes or classify data. These components function in unison to produce a functional and valuable instrument for businesses or organizations.
Think of it like building a puzzle! We start by comprehending the specific business needs and goals that sparked the ML project. We envision the perfect user interface, ensuring it is user-friendly and easy to navigate.
We design our system for scalability, maintainability, performance, and security.
We carefully consider the infrastructure that enables the delivery of the ML system. This includes the hardware, software, and networks that support the system, as well as any cloud resources or other external services that it relies on.
But building a software system with machine learning adds an extra layer of complexity! We have to think of at least three more things:
Data Pipelines
First, we have to take into account the data pipelines that feed data into the system. These pipelines are responsible for extracting data from various sources, cleaning and preparing the data, and storing it in a format that is ready to be used by the ML algorithms. Data pipelines can be complex and may require significant resources to set up and maintain.
ML Models
Another unique aspect of ML-intensive software systems is ML models. We have to build the logic for developing, monitoring, and updating these models. This includes the processes and procedures for training and evaluating models and the mechanisms for deploying new models and retiring old ones.
Maintenance
Finally, the maintenance and support of the ML system is an ongoing process. This includes tasks such as monitoring the system for errors or degrading performance due to a shift in data distribution from development time to production. Therefore, it is especially important to design ML systems for observability, which is the ability to monitor the ML model performance in production.
Conclusion
In conclusion, an ML system in production involves many components beyond just the algorithms it uses. It includes business requirements, user interfaces, data pipelines, ML models, and ongoing maintenance and support. These components work together to create a functional and reliable tool for solving business problems using machine learning. It’s a challenging but rewarding journey.
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