Opportunity Identification

This is the most critical step for any artificial Intelligence project or initiative. You need a direction for your organization in an effort to successfully deploy A.I. into your organization and realize actual return on investment. This is also the base for the Operationalization of Machine Learning.

Data Engineering

The Data Engineering pipeline includes a sequence of operations on the available data that leads to supplying training and testing datasets for the machine learning algorithms:

• Data Ingestion
• Exploration and Validation
• Data Cleaning
• Data Labeling
• Data Splitting

Model Engineering

The core of the ML workflow is the phase of writing and executing machine learning algorithms to obtain an ML model. The Model Engineering pipeline includes a number of operations that lead to a final model:

• Model Training
• Model Evaluation
• Model Testing
• Model Packaging

Model Deployment

Once we trained a machine learning model, we need to deploy it as part of a business application such as a mobile or desktop application. The ML models require various data points (feature vector) to produce predictions. The final stage of the ML workflow is the integration of the previously engineered ML model into existing software.

• Model Serving
• Model Performance Monitoring
• Model Performance Logging

Continuous System Delivery and Improvement

CI/CD or CICD is the combined practices of continuous integration and continuous delivery or continuous deployment. Most modern A.I. solutions require retraining and redeployment for everincreasing accuracy, so teams need a consistent mechanism to itrain, validate and deploy changes. This is considered MLOps, Not only does this increase the models accuracy over time, it also buffers against model degridation.