Knowledge Graph — Coursera Notes › Academic disciplines › Computer Science / Information Technology › Machine Learning
AI ML Engineering Workflow
concept · part of Machine Learning
The AI ML engineering workflow is a systematic process that most AI/ML projects follow, starting with data collection and moving through data pre-processing, model development, training, evaluation, and deployment. It ensures projects move beyond development to successful production. Mastering this workflow is key to building scalable, impactful AI solutions.
Inside AI ML Engineering Workflow (7)
- Data Collection — Data collection is the first stage of the AI ML workflow, where data is gathered from various sources such as databases, sensors, or user inputs.
- Data Pre-processing — Data pre-processing involves cleaning, transforming, and preparing data for model development.
- Deployment — Deployment integrates the trained model into a real-world application, such as a web service, mobile app, or automated system.
- Evaluation — Evaluation assesses model performance using a test set (unseen data).
- Model Development — In model development, engineers choose an appropriate model architecture based on the problem.
- Monitoring and Retraining — After deployment, ongoing monitoring is essential to ensure the model continues to perform.
- Training — During training, the model is fed processed data and iteratively learns patterns by adjusting its weights.
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