AI/ML Engineering Services in C Scheme
A model that's 95% accurate in a notebook and never reaches production is worth nothing. For C Scheme teams, I build the full pipeline — training, deployment, monitoring — so ML actually ships.
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- Transparent pricing
- No lock-in contracts
- Proven results
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The pipeline is the product
The pipeline that retrains a model, the monitoring that catches drift, and the API that serves it reliably — that's where most ML projects for C Scheme businesses actually fail. A model that's 95% accurate in a notebook and never reaches production is worth nothing; I build the whole pipeline, not just the model.
What's included
Model training and tuning
Classical ML and deep learning models trained and tuned against your C Scheme business's real data and evaluation criteria.
Techniques used
- Gradient-boosted trees (XGBoost) for structured data
- Deep learning (PyTorch) where the problem genuinely needs it
- Hyperparameter tuning against a real validation set
CI/CD for models
Versioning and reproducible pipelines so retraining your C Scheme models is routine, not a manual, error-prone process.
Drift detection and retraining
Automated monitoring that catches model drift before it silently degrades decisions your C Scheme business depends on.
Monitoring components
- Input distribution drift alerts
- Performance degradation tracking
- Scheduled or triggered retraining
Clean, documented APIs
Models served through documented APIs your C Scheme product team can integrate without needing to understand the internals.
How the engagement runs
Process
Problem framing
We define the exact prediction task and success metric for your C Scheme business before any model gets trained.
Training & validation
Models are validated against held-out and, where possible, live data before deployment.
Deployment & monitoring
Production deployment on your C Scheme team's existing cloud, with drift monitoring from day one.
Who this is for
Product teams needing ML in production
C Scheme teams that have a model idea validated in a notebook but no path to shipping it reliably.
Businesses with an existing but fragile ML setup
C Scheme companies whose current models silently degrade with no monitoring in place.
Tools & technology
Cloud & infrastructure
Deployment targets
AWS, GCP or Azure — fitted to your C Scheme team's existing stack
No forced migration to a new cloud just to accommodate the ML pipeline.
Note
Computer vision and NLP work, including fine-tuning smaller models, is available where a full LLM isn't the right tool for your C Scheme use case.
Quick answer
AI/ML engineering here means building the entire pipeline around a model — training, CI/CD, drift monitoring and a served API — not just training a model that stays in a notebook. For a C Scheme business, that's what turns a 95%-accurate prototype into something your product actually depends on every day, with retraining and monitoring that keep it accurate as real-world data shifts.
How this compares to a freelancer who only trains models
- A model-only freelancer hands your C Scheme team a notebook; this delivers CI/CD, versioning and a documented API your product team can integrate directly.
- Notebook-only work has no drift monitoring; pipelines here include input distribution and performance degradation tracking so your C Scheme business's models don't silently decay.
- A freelancer trains once and moves on; retraining here is automated and scheduled, so your C Scheme models stay current without manual re-runs.
- Deployment is often left to your team; this includes deployment on your C Scheme business's existing cloud (AWS, GCP or Azure) with monitoring from day one.
Why C Scheme teams choose Deepak Suhag
Full production pipelines — training, deployment, monitoring — so ML actually ships and keeps working, not just a proof of concept.
How it works
Simple, transparent process — from first contact to measurable results.
Discovery Call
30-minute deep dive into your business, goals, and current marketing channels. No prep needed.
Strategy Blueprint
Full-funnel channel map, budget allocation, KPIs, and a 90-day growth roadmap.
Hands-on Execution
Campaign setup, conversion tracking, creative briefs, and continuous A/B testing.
Scale & Optimise
Weekly ROAS reports, budget reallocation, and monthly strategic reviews.
Tools & platforms
The exact stack I use daily across growth marketing, web development, AI, and automation — no guesswork, no vendor lock-in.
Why work with Deepak
Here's what makes this different from every other option in C Scheme.
Practitioner, not a consultant
I manage live campaigns daily — not just strategy decks. Your budget is treated like my own money.
Full-funnel accountability
From first click to closed deal. I track CAC, LTV, and ROAS — not just impressions or CTR.
AI & automation-first approach
I build marketing systems that scale without scaling headcount — using n8n, Make, and AI integrations.
No agency layers
No account managers, no junior execs. You work directly with me — every strategy call, every week.
Everything you need to know
Still have a question that isn't answered here? Reach out directly — I respond to every inquiry personally.
Ask a question01Can you deploy on our existing cloud in C Scheme?
Yes — AWS, GCP, or Azure; I fit into your existing infrastructure rather than forcing a new stack.
02What ML frameworks do you use?
scikit-learn, PyTorch, and XGBoost, depending on the problem — I pick the simplest thing that works reliably.
03Do you do computer vision / NLP work for C Scheme clients?
Yes, both — including fine-tuning smaller models where a full LLM isn't the right tool.
04How do you avoid model rot?
Drift monitoring, scheduled retraining, and a documented pipeline your team can run without me.
05What does AI/ML engineering cost for a C Scheme business?
Pricing depends on model complexity and how much pipeline and MLOps work is needed around it — a structured-data model on existing clean data costs less than a deep learning system with new data infrastructure. Share your C Scheme business's setup for a specific quote.
06How is this different from hiring an in-house ML engineer in C Scheme?
An in-house hire in C Scheme takes months to recruit and typically specializes in either modeling or infrastructure, not both; this delivers the model, the CI/CD pipeline and the monitoring together, with documentation so your team can eventually own it.
07How long until a model is live in production for our C Scheme business?
A first production deployment, including drift monitoring, typically takes 6-10 weeks depending on data readiness and how much of your C Scheme business's existing infrastructure the pipeline needs to integrate with.
08Is this a good fit if we only have a rough model idea, not clean data yet?
If your C Scheme business has no usable historical data yet, the first step has to be data collection and pipelining — modeling can't meaningfully start before that. This engagement can include that groundwork, but it extends the timeline.
09What happens in the first week for a C Scheme client?
The first week defines the exact prediction task and success metric for your C Scheme business, and audits what data already exists to support it, before any model training begins.
I've spent 10+ years managing campaigns across D2C, B2B, and SaaS — from small monthly budgets to large seven-figure spends. What I've learnt: most businesses don't need more ad spend. They need smarter systems. That's what I build.