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In the world of Machine Learning, understanding the feedback loops is crucial for success. This article explores the main stages of a Machine Learning project and the types of feedback that flow between them. From the ideation stage to production deployment and monitoring, each stage plays a vital role in continuous improvement. By recognizing the different types of feedback and leveraging them effectively, organizations can maximize the impact of their ML systems and ensure robustness for long-term success.
What are the Main Feedback Loops in Machine Learning Lifecycle Systems?
A typical Machine Learning project consists of four main stages:
- Ideation stage: This is where business and data professionals come together to generate a hypothesis that is then evaluated for feasibility.
- Experimentation: This stage involves working with available data assets to build a Proof of Concept (PoC) model.
- Productionization: The PoC model is transformed into a Machine Learning System, often deployed as an ML Pipeline in a production environment. This stage enables Continuous Training (CT) and integrates the ML Service into the broader software system.
- Monitoring: In this stage, the performance of the models running in production is tracked. It serves as the feedback loop for the previous stages and allows for continuous improvement.
Within this lifecycle, there are different types of feedback that flow from stage 4 (Monitoring) to stages 2 (Experimentation) and 3 (Productionization).
A. Feedback from Monitoring to Experimentation Stage:
- Online Testing: This involves monitoring a business metric in production to evaluate the model’s performance. The choice of metrics depends on the specific business goals, such as conversion rate, click-through rate, or revenue per user.
B. Feedback from Monitoring to Deployment Stage:
- B.1: Regular service monitoring: This type of feedback focuses on monitoring service-related metrics like latency, response codes, and hardware consumption.
- B.2: Feature and concept drift monitoring: This feedback is specific to Machine Learning Systems and involves monitoring for changes in the distribution of features and concepts over time. It helps ensure the robustness of the ML System.
Both types of feedback, A and B, are crucial for continuous improvement of the ML product’s business impact and the overall robustness of the ML System.
By understanding and leveraging these feedback loops, machine learning practitioners can enhance the effectiveness and reliability of their models throughout the entire lifecycle.