Integrating heterogeneous data sources
Machine Learning Engine Activated
Initiate Validations of Models
Monitoring for Model drift, performance degradation and bias
amplification
Beginning Tesseract Unfolding
Conducting Sensititivy Analysis to Identify Key Drivers
Databases Stabilized
Querying knowledge sources
Identifying Critical Nodes and Single Points of Failure
Simulating Propagation Patterns and Adding Stress
Conditions
Reconfiguring Neural Networks
Containerizing Model Deployment
Initiating Data Pipelines
Ingesting Data and Normalizing
Starting Supervised and Unsupervised Learning
CI/CD Pipelines Created for Model Deployment
Calibrating Data Drift Detection Methods
Establishing Real-time Inference APIs
Governance Controls In Place and Aligned with Regulatory & Compliance
Policies