ML Models That Survive Production Data
We build machine learning systems that don't just hit a benchmark on a clean notebook and quietly rot in production six weeks later. Real data pipelines, real drift monitoring, real rollback plans, and models trained by engineers who've watched a 0.94 F1 score collapse to 0.61 the day a vendor changed an upstream schema.
You don't need a Kaggle notebook.
You need a model that holds up on Tuesday.
Most ML projects don't fail in training. They fail the moment the training set stops looking like the real world. We build for the messy reality of production ML: schema drift, label noise, feedback loops, late-arriving data, and the model owner who left the company three months ago and took the retraining script with them.
Great offline metrics, useless online
AUC of 0.93 in the notebook, business KPI flat. Train/serve skew, leakage, or a feature that doesn't exist at inference time. Nobody can explain the gap and the project quietly dies.
The model drifted and nobody noticed
Distribution shift over six months, performance bleeding 1% a week, no monitoring on inputs or outputs. You only find out when a customer escalates.
It only runs on the data scientist's laptop
A pickle file, a Jupyter notebook, an undocumented conda env, and a Slack message. No CI, no reproducibility, no path to production beyond "let's containerize it some day".
What You Actually Get
No vague deliverables. Here's exactly what lands in your hands.
A model in production, behind an API
Versioned, containerized, autoscaled. Inference endpoint with auth, rate limits, latency SLOs, and a rollback button that actually works.
A reproducible training pipeline
Code, data versioning, feature store, training jobs, eval reports — all in CI. Anyone on your team can retrain from a single command and get the same model.
Drift, quality, and business dashboards
Input drift, prediction drift, label delay, segment-level performance, and the business KPI the model is supposed to move. Alerts wired into your on-call.
A model card and a runbook
What it does, what it doesn't, how it was trained, known failure modes, retraining cadence, and the on-call playbook for when it misbehaves at 2AM.
A Real Applied ML Team
Shipping ML well takes more than a notebook wizard. Six roles you get on every Pillai Infotech ML build.
Applied Research Lead
Reads papers, but ships things. Picks the boring model that works over the SOTA model that doesn't. Knows when a logistic regression beats a transformer.
ML Platform Engineer
Builds the feature store, training infra, model registry, and serving layer. Knows Kubeflow, Ray, Airflow, and when to throw all of them out for a cron job.
Data Engineer
Owns the pipelines that feed the model. Schema contracts, late data, backfills, dedup, PII scrubbing. The reason your training set actually matches reality.
ML Reliability Engineer
Drift detection, shadow deployments, canary rollouts, A/B harness, kill switches. Treats models like services, not science projects.
Responsible AI Lead
Bias audits, fairness slices, explainability (SHAP, LIME, counterfactuals), consent and data lineage. Files the paperwork so legal and compliance don't hold you up.
Product-ML Translator
Turns "we want to use AI" into a measurable hypothesis with a baseline, a target metric, and a kill criterion. Stops the project before it becomes a science fair.
You See Everything. In Real Time.
Every Pillai Infotech project comes with a dedicated client dashboard. Kanban boards, live logs, test results, meeting notes — it's all visible the moment it happens. No status-report theatre, no "we'll get back to you", no surprises at the demo. You work with us like you work with your own team.
Kanban Board, Live
Every epic, every story, every task — visible on your dashboard. Drag, comment, reprioritize. It's the same board our team works from.
Documented Everything
Every decision, spec, API contract, and architecture diagram lives in the dashboard. Searchable, versioned, linked to the tasks they shaped.
Live Logs & Test Results
Build logs, deployment logs, test suite results — streamed to your dashboard the moment they run. You never have to ask "did the build pass?"
Meetings → Tasks, Automatically
Every meeting is recorded, transcribed, and every action point is auto-converted into a tracked task assigned to the right person. Nothing gets lost between calls.
Sprint Burndown & Velocity
See exactly how much work is done, how much remains, and our velocity over time. If a sprint is slipping, you see it the same moment we do.
Comment, Approve, Decide — In-Place
Comment on any task, approve designs, sign off on specs, and raise blockers directly in the dashboard. Everything tied to the work, not buried in email threads.
ML Systems We Know How to Ship
We pick the model and the architecture to match the problem, not the other way round.
🎯 Recommendation & ranking
Two-tower, gradient-boosted rankers, contextual bandits. Online learning loops, candidate generation, business-rule overrides, cold-start handling.
🚨 Fraud & anomaly detection
Real-time scoring, graph features, feedback delay handling, label noise. Tuned for precision-at-K because false positives have real costs.
🔮 Forecasting & demand planning
Hierarchical forecasting, intermittent demand, holiday and promo effects, prediction intervals — not just point estimates that everyone ignores.
📝 NLP & document intelligence
Classification, extraction, summarization, semantic search. Fine-tuned where it wins, retrieval-augmented where it wins, rules where rules win.
🎚️ Pricing & elasticity models
Causal inference, uplift modeling, constrained optimization. We'll tell you when an A/B test beats a model, and when it doesn't.
🧬 LLM-augmented pipelines
When a small LLM in the loop beats a custom classifier — and when it doesn't. Cost-aware routing, eval harnesses, hallucination guards.
The ML Stack We Use
Boring tools where they win. Cutting-edge where they earn it.
Modeling
Platform & MLOps
Serving
Data & Monitoring
A Six-Stage ML Delivery Process
Built around the reality that your data, not your model, decides whether this works.
Problem Framing & Baseline
What decision does this model change, what's the business KPI, what's the dumb baseline (rules, heuristic, last week's number) we have to beat. Decided in week one, in writing.
Data Audit
Schemas, freshness, label quality, leakage risk, PII exposure, segment coverage. We tell you honestly whether the data is good enough before we model.
Model Build & Offline Eval
Iterate on a holdout that mirrors production. Slice metrics by segment. Document every assumption. No leaderboard chasing.
Shadow & Canary
Deploy alongside the existing system, log predictions, compare outputs. No customer impact until we see the model behave on real traffic.
Production Rollout
Phased traffic, kill switch, rollback plan, drift and KPI dashboards live before the first real user sees a prediction.
Monitor & Retrain
Drift checks, performance reviews, scheduled retraining, eval gates in CI. Weekly review for the first 90 days, then handed off with a runbook.
Three Ways to Engage
ML projects don't fit one shape. Pick the one that matches your stage.
ML Feasibility Sprint
Two-week engagement to audit your data, build a baseline, run a quick model, and tell you honestly whether ML is the right tool here.
- Data + label audit
- Baseline + first model
- Honest go / no-go in writing
Fixed-Scope ML Build
End-to-end model delivery from problem framing to production serving, with monitoring, retraining, and post-launch warranty.
- Fixed scope, fixed price
- Typical: 12–24 weeks
- 60-day post-launch warranty
Embedded ML Squad
A dedicated ML + data + platform squad working alongside your team on a continuous model roadmap.
- ML + Data + Platform + PM
- Monthly retainer, scale up/down
- Best for: ongoing model portfolio
Honest Answers to ML Reality Questions
The questions every smart buyer asks before signing. Here's what we tell them.
Do we even need machine learning for this?
Often no. If a SQL query, a rules engine, or a thresholded heuristic gets you 85% of the way, that's the right answer — cheaper, debuggable, no drift. We say no to ML projects regularly. The feasibility sprint exists exactly so you don't spend six figures on a model you didn't need.
Can you use our existing data warehouse?
Yes — that's most ML projects. Snowflake, BigQuery, Redshift, Databricks, Postgres, whatever you have. We build on top of your warehouse, not around it. If the data needs ML-specific features (point-in-time joins, feature store, backfills), we'll build that layer.
How do you handle model drift?
Three layers. Input drift on the feature distributions. Prediction drift on the score distribution. Performance drift on the labeled outcomes when labels arrive. All three feed dashboards and on-call alerts. Retraining is either scheduled or drift-triggered, with eval gates in CI so a worse model can't silently replace a better one.
How big does our dataset need to be?
Smaller than you think for tabular problems with strong signal. Larger than you hope for fine-grained NLP or vision. We'll tell you in week one whether the data volume and label quality are enough — and whether weak supervision, transfer learning, or just buying labeled data is the right fix.
Do you fine-tune LLMs or use RAG?
Both, when each one wins. Fine-tuning for narrow, repetitive, latency-sensitive tasks where you can amortize the training cost. RAG when the knowledge changes weekly and explainability matters. Often a small classifier beats both. We benchmark before recommending.
What about explainability and bias?
SHAP for tabular, attention and counterfactuals for deep models, slice-level metrics for fairness. We document known failure modes in the model card and bake fairness checks into the eval gates. If your domain is regulated (credit, hiring, healthcare), explainability is a first-class requirement, not an afterthought.
Who owns the model and the training code?
You do. Code in your Git org. Models in your registry. Training data in your warehouse. If we walked away tomorrow, your next ML team could retrain from a single command and ship an update on Monday.
How do you price an ML project?
Feasibility sprints are fixed price. Production builds are fixed scope after the sprint, because by then we know the data. Embedded squads are monthly retainer. We don't bill against guesses — that's how ML projects blow budgets.
What if the model just doesn't work?
It happens. The feasibility sprint exists to find that out cheaply. If the data isn't there, we'll tell you in two weeks for the cost of a sprint, not in six months for the cost of a build. That's the deal — honest go/no-go is the whole point.
Can you sign an NDA before we share details?
Always. NDA before the first call. Data and model assets stay under your control. We're happy to work inside your VPC or cloud account if compliance requires.