MLRun

Define features in the Feature Store (e.g., avg transaction amount last 24h).

Train an XGBoost model using historical transaction data via MLRun Job.

Deploy the model as a Nuclio serving function.

Enrich real-time events with features from the Online Feature Store.

Trigger alerts or block transactions based on inference scores.

Ingest IoT sensor data using MLRun's streaming capabilities.

Apply rolling window transformations to generate time-series features.

Train LSTM models for anomaly detection.

Monitor model accuracy and equipment health in a Grafana dashboard.

Automate retraining when model performance degrades.

Consolidate CRM and web activity data into MLRun.

Use MLRun pipelines to run hyperparameter tuning (AutoML).

Select the best model and register it in the Model Registry.

Schedule weekly batch inference jobs to update churn scores.

Export scores to Salesforce/Marketing automation via webhooks.

Deploy a distributed processing job using Dask within MLRun.

Tokenize and vectorize text data across a cluster of nodes.

Fine-tune a BERT model on the processed dataset.

Wrap the model in a serverless inference endpoint.

Auto-scale the workers based on the length of the processing queue.

Set up an online feature store to track real-time user clicks.

Deploy a multi-stage graph: Retrieval, Ranking, and Re-ranking.

Update the ranking model daily using a scheduled MLRun workflow.

Use A/B testing functions to compare model versions in production.

Monitor user engagement metrics through MLRun's integrated tracking.