AI/ML Engineering Services in Panki
A model that's 95% accurate in a notebook and never reaches production is worth nothing. For Panki 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 Panki 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 Panki 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 Panki 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 Panki 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 Panki 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 Panki 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 Panki team's existing cloud, with drift monitoring from day one.
Who this is for
Product teams needing ML in production
Panki 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
Panki companies whose current models silently degrade with no monitoring in place.
Tools & technology
Cloud & infrastructure
Deployment targets
AWS, GCP or Azure — fitted to your Panki 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 Panki 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 Panki 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 Panki 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 Panki business's models don't silently decay.
- A freelancer trains once and moves on; retraining here is automated and scheduled, so your Panki models stay current without manual re-runs.
- Deployment is often left to your team; this includes deployment on your Panki business's existing cloud (AWS, GCP or Azure) with monitoring from day one.
Why Panki 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 Panki.
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 Panki?
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 Panki 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 Panki 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 Panki business's setup for a specific quote.
06How is this different from hiring an in-house ML engineer in Panki?
An in-house hire in Panki 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 Panki business?
A first production deployment, including drift monitoring, typically takes 6-10 weeks depending on data readiness and how much of your Panki 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 Panki 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 Panki client?
The first week defines the exact prediction task and success metric for your Panki 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.