Feature stores, model serving, drift detection
Trusted by 100+ innovative teams
What we build
We architect and implement ML pipelines on Kafka, Pub/Sub, and Kinesis that handle production scale with the reliability your models depend on.
Built for teams like yours
How we deliver
Map your workflows, identify high-impact opportunities, and quantify ROI potential.
Build a focused MVP for your highest-impact use case in 4-6 weeks.
Harden, monitor, and expand — leveraging existing infrastructure for each new capability.
4-8 weeks
pilot to production
95%+
milestone adherence
99.3%
SLA stability
Real-Time ML Pipeline Architecture Implementation
Use the same rollout pattern we apply in production programs: architecture review, risk controls, and measurable milestones from pilot to scale.
4-8 weeks
pilot to production timeline
95%+
delivery milestone adherence
99.3%
observed SLA stability in ops programs
Deep dive
Real-time ML is more than running predictions from an HTTP endpoint. A production real-time ML system is a chain: features are computed and stored, models are versioned and served, predictions are logged, drift is detected, and retraining loops keep the system from degrading silently.
The hard part isn't the model. The hard part is the platform around it that delivers consistent features at sub-100ms latency, deploys models without breaking production, and gives the team enough observability to debug a regression at 2am.
We help engineering teams build that platform — not as one-off projects, but as durable infrastructure their ML and product teams operate together.
A typical production real-time ML stack has six layers:
Each layer has multiple credible implementations. We help teams pick a coherent stack rather than the trendiest tool at each layer.
The feature store is the single most consequential piece of real-time ML infrastructure. Without one, you eventually hit training-serving skew — the features your model trained on are subtly different from the features it sees in production. Quality degrades silently. Debugging is painful.
A real feature store enforces:
Tools we deploy:
Features that depend on recent activity ("clicks in the last 5 minutes," "purchase count today") require stream processing. We choose between:
The key constraint: features computed in streaming must be reproducible from the offline historical data, or you end up with skew. We invest in this parity from day one.
Different latency budgets and traffic shapes call for different serving patterns:
The decision is about the latency budget and update frequency of predictions, not about which tool sounds best.
Latency optimization beyond model architecture itself:
Profile end-to-end before assuming the model is the bottleneck. Often the network round trip, feature fetch, or serialization dwarfs the inference itself.
Production ML systems must be able to release new models without breaking traffic.
Patterns we ship:
Without this infrastructure, every model release becomes a production risk. With it, releases compound into improvement.
Models degrade. The world changes; the data the model was trained on stops representing reality.
We instrument:
When drift is detected, retraining can be automated (a new model trained on recent data, validated against the existing one, promoted if better) or alert-only (the team is notified, retraining is a deliberate decision).
For most engagements, real-time ML engagements typically run 8–14 weeks:
We do not deliver Jupyter notebooks. We deliver platforms the team can operate after we leave.
Real-time ML is platform engineering as much as it is data science. Teams that treat it that way ship reliably; teams that treat it as a model-deployment problem keep firefighting.
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.
Explore related services, insights, case studies, and planning tools for your next implementation step.
Delivery available from Bengaluru and Coimbatore teams, with remote implementation across India.
Case Studies
Deel uw projectdetails en wij nemen binnen 24 uur contact met u op voor een gratis consultatie — zonder verplichtingen.
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