The Realistic Picture of a Machine Learning Model Lifecycle

In an average organization today, the realistic picture of a machine learning model lifecycle involves many different people with completely different skill sets who might use entirely different tools. Here is the big picture.

The diagram above can be broken down into the following:

Business Question

• Define Objectives: Collaborate with stakeholders to understand the specific business goals and translate them into clear, answerable data science questions. These questions should guide the entire project.

Develop Models

• Identify Data Sources: Determine where the relevant data is located and how to access it. This may involve internal databases, external APIs, or even manual data collection methods.
• Data Preparation: Clean, transform, and format the data to prepare it for analysis and modelling. This might include handling missing values, inconsistencies, and ensuring data quality.
• Feature Engineering: Create new features from existing data that can potentially improve model performance, as well as being understandable by the model. This might involve feature extraction, transformation, or selection.
• Model Selection & Training: Choose an appropriate e.g, statistical learning or machine learning algorithm based on the problem type and data characteristics. Train the model on a portion of the data, aiming to optimize its ability to address the business question.
• Model Evaluation & Comparison: Evaluate the trained model’s performance on a separate hold-out set of data. This involves assessing metrics like accuracy, generalizability, and potential biases. You might also compare different models to identify the best performer.

Prepare for Production

• Model Packaging: Package the chosen model in a format suitable for deployment in a real-world setting. This might involve containerization using tools like Docker for easy transfer and execution.
• Infrastructure Setup: Prepare the computing infrastructure where the model will operate in production. This may involve cloud platforms, on-premise servers, or a combination of both, depending on project needs.
• API Design (if applicable): If the model will be accessed through an API, design and implement a user-friendly interface for integrating the model into applications.

Develop to Production

• Model Packaging & Containerization: Package the chosen model using containerization technologies like Docker. This creates a standardized unit that encapsulates the model code, dependencies, and runtime environment. This simplifies deployment across different environments and ensures consistent behavior.
• Elastic Scaling: Deploy the containerized model to a platform that supports elastic scaling. Cloud platforms like Google Cloud Platform (GCP), Amazon Web Services (AWS), or Microsoft Azure offer features for automatically scaling compute resources up or down.
• CI/CD Pipeline Integration: Integrate the model deployment process into a Continuous Integration and Continuous Delivery (CI/CD) pipeline. This automates tasks like code building, testing, and deployment. Changes to the model code or its dependencies trigger the pipeline, streamlining the process of pushing updates to production on demand. This ensures the model can handle fluctuating workloads without performance degradation.

Monitoring and Feedback Loop

• Performance Monitoring: Continuously monitor the model’s performance in production using relevant metrics. Track for potential issues like degradation in accuracy, data drift (changes in data distribution), or concept drift (changes in the underlying problem).
• Alerting & Feedback: Implement a system for generating alerts if performance metrics fall outside acceptable ranges. This triggers investigation and potential re-training of the model.

Continuous Improvement: The data science workflow is iterative. Insights gained during monitoring can inform improvements in data preparation, feature engineering, or model selection. This feedback loop ensures the model remains effective in a dynamic environment.