Get models out of notebooks and into production with monitoring that catches drift before customers do
watsonx.data for lakehouse-style analytics, MLflow for experiment tracking and model registry, Milvus and Pinecone vector stores for RAG pipelines, and Apache Airflow for orchestration. We build the data infrastructure that turns experimental ML into reliable production systems with automated retraining, A/B deployment, and real-time monitoring.
MLOps (Machine Learning Operations) is the practice of deploying, monitoring, and maintaining ML models in production. Without MLOps, models degrade silently, retraining is manual, and there is no audit trail. We build automated pipelines using watsonx.data, MLflow, and Kubeflow that handle data versioning, model training, deployment, and monitoring as a single automated workflow.
An MLOps platform assessment takes 2-3 weeks at $5,000. A full platform buildout with automated training pipelines, model registry, and monitoring typically runs 10-16 weeks at $20,000-$35,000. Feature store and vector database integration adds $10,000-$15,000 depending on data volume.
We deploy Milvus for high-throughput similarity search, Elasticsearch with vector extensions for hybrid text+vector queries, and watsonx.data for integrated lakehouse analytics with vector capabilities. The right choice depends on your query patterns, data volume, and existing infrastructure.
Yes. We build data pipelines that connect to Snowflake, Databricks, BigQuery, Redshift, and traditional databases. Our MLOps platforms pull training data from your existing warehouse and push predictions back, without requiring data migration.