Real-Time ML Pipeline Architecture
Production event streaming for machine learning, from feature stores and real-time inference to model serving and A/B testing infrastructure. We architect and implement ML pipelines on Kafka, Pub/Sub, and Kinesis that handle production scale with the reliability your models depend on.
Our implementation approach covers the full spectrum of real-time ml pipeline architecture partner, bengaluru.
Apache Kafka deployment and tuning for ML workloads
Google Pub/Sub integration with Vertex AI and Dataflow
Real-time feature store architecture (Feast, Tecton, custom)
Online inference pipeline development
Event-driven model serving with A/B testing
Stream processing for feature engineering (Kafka Streams, Flink, Dataflow)
ML data pipeline observability and monitoring
Confluent Cloud implementation and management
Event replay architecture for model retraining
Cost optimization for high-throughput ML event streams
Schema Registry design and governance for ML data contracts
Multi-region disaster recovery for ML event pipelines
Exactly-once semantics implementation for accurate feature computation
Lambda and Kappa architecture design for feature stores
Common questions about 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.
We build production-ready real-time ml pipeline architecture partner, bengaluru systems designed to scale.
We approach every project with production readiness in mind—proper error handling, monitoring, and scalability from day one.
We help you decide what to build custom and what to integrate. Not every problem needs a custom solution.
Our team brings deep experience in building similar systems, reducing risk and accelerating delivery.
Share your project details and we'll get back to you within 24 hours with a free consultation—no commitment required.
Boolean and Beyond
825/90, 13th Cross, 3rd Main
Mahalaxmi Layout, Bengaluru - 560086
590, Diwan Bahadur Rd
Near Savitha Hall, R.S. Puram
Coimbatore, Tamil Nadu 641002