From the course: MLOps Essentials: Model Development and Integration
Machine learning life cycle
From the course: MLOps Essentials: Model Development and Integration
Machine learning life cycle
- [Instructor] Let's begin the course by reviewing the activities that happen when building machine learning solutions. A machine learning life cycle charts the journey of an ML application. It starts from the concept of the problem we are trying to solve. From there, we build a model with training data. The model is deployed and used in production. Then, periodic improvements happen to the model over time. The ML life cycle is a cyclic process. The process continues for multiple iterations depending upon the use case. Continuous refinements happen during the life of a model, new training data, new user requirements and model decay can also cause the model to be retrained and improved. Let's review the machine learning life cycle. It starts with requirements for the model usually charted by a product owner. The requirements are then used to design the workflow for executing the project. First, training data is acquired and made available for machine learning. Feature engineering is done on the training data to cleanse, transform and extract useful features. This is then used to train the model. The model goes through testing and evaluation. The model is continuously refined until desired performance goals are achieved. The model is then deployed for inference and is used to predict the business workflows. During inference, more data is collected from the production deployments. This is then labeled to create additional training data. New training data again triggers feature engineering and model training operations. The process continues in a cyclic fashion to keep the model optimized to accommodate business environment changes. In this course, we are not going to focus on the actual process of building a model. Rather, we are going to look at the ecosystem around this life cycle that helps us execute this life cycle with efficiency and control.
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