Real-Time ML Pipeline Architecture Partner, Bengaluru

We evaluate your specific requirements, event replay needs, ordering guarantees, latency constraints, cloud provider, and team ops capacity. We prototype the critical path on both platforms, measure real performance against your workload, and recommend with concrete data. Most decisions are clear once you match workload characteristics to platform strengths.

A focused real-time inference pipeline (event ingestion, feature lookup, model serving, response delivery) takes 4-6 weeks. A full ML platform with feature store, stream processing, schema governance, model registry, and A/B testing takes 12-16 weeks. We work alongside your ML team throughout and transfer operational ownership at the end.

We offer both implementation-only and ongoing management engagements. For teams that want to hand off Kafka operations, we provide monitoring, maintenance, upgrades, and capacity planning. For teams building internal capability, we train your engineers and transition operations over 4-8 weeks with paired working and documented runbooks.

Yes, migration from batch to streaming is one of our core engagement types. We design a parallel run strategy where streaming features and batch features are computed simultaneously and validated against each other before cutover. This de-risks the migration and lets you validate that streaming feature accuracy meets your model quality requirements before you retire the batch pipeline.

We have production experience with Feast (self-managed and cloud-managed), Tecton, Vertex AI Feature Store, Hopsworks, and custom feature stores built on Redis, Bigtable, DynamoDB, and Cassandra. We recommend based on your team's operational preferences, your cloud provider, and your feature serving latency requirements.

We implement schema governance through Confluent Schema Registry (for Kafka) or a shared Protobuf repository with automated compatibility checks (for Pub/Sub). All schema changes go through a compatibility check in CI before merge. Breaking changes trigger an automatic pipeline block. We also implement schema versioning in the feature store so models can declare their required feature schema version and receive compatible features even during a migration.