How long does machine learning development services take? A realistic timeline

Most machine learning development services run eight to sixteen weeks from kick-off to a model serving real decisions. A narrow single-model build lands near eight weeks. Several data sources, a retraining loop and a human approval workflow push it to sixteen. Eazyware's AI/ML Development programme starts at $17,500 or ₹11,20,000 against a fixed date.

That range is not a hedge. It is the difference between two genuinely different projects, and you can work out which one you have before you sign anything. This article walks the machine learning development services timeline phase by phase, states what each phase produces, marks what can run in parallel, and lists the seven things that reliably add weeks.

What the eight-to-sixteen-week range actually covers

A machine learning development service is the work of turning a business decision into a model that runs on live data, plus the plumbing that keeps it honest. The clock starts at kick-off and stops when the model is serving predictions into a system a person or process actually uses, with monitoring attached.

It does not stop at a notebook with a good AUC. That distinction is where most published timelines go wrong. Getting a model to score well on a historical extract is often two or three weeks of work. Getting it into a system, with a retraining path, drift alerts, a rollback and an owner, is the other six to thirteen.

The modelling itself is rarely the long pole. On the builds we have run, feature engineering and data access together consume roughly twice the calendar time of model selection and tuning. If your data lives in one warehouse with documented tables, you are at the fast end. If it lives in three operational databases, a vendor export and somebody's spreadsheet, you are not.

A phase-by-phase machine learning development services timeline

Here is how a typical mid-sized build divides. Durations assume one ML engineer, one data engineer and a named business owner who can answer questions within a day.

Add the serial phases and you get eight weeks at the floor. Add the upper bounds of the phases that cannot overlap and you get sixteen. Anything a vendor quotes below six weeks is either skipping shadow running or delivering a notebook.

Two dates are worth fixing in writing before kick-off: the day shadow running is reviewed, and the day handover completes. Everything else can slip by a few days without consequence, but those two are what your business case depends on. A supplier who will commit to both inside a fixed-price statement of work has thought about the schedule. One who offers a single end date with no intermediate checkpoint has not, and the overrun will surface too late to manage.

What adds weeks, and roughly how many

Seven things move the date. Price each one honestly during scoping rather than discovering it in week nine.

• No labelled outcomes. If nobody recorded what actually happened, you are building a labelling process first. Add two to five weeks depending on volume and who does the labelling.
• Data spread across systems. Each additional source adds roughly a week of access, reconciliation and identity matching. Three sources is normal; six is a data project wearing an ML hat.
• Personal or regulated data. A DPDP Act review, a data processing agreement or an RBI outsourcing check adds one to three weeks and usually sits with your legal team, not ours.
• A decision that needs approval. If a human signs off every prediction, you are also building a review queue and an audit trail. Add two to three weeks.
• Class imbalance or rare events. Fraud, churn at low base rates and equipment failure need far more careful validation. Add one to two weeks of error analysis.
• A legacy system of record. Writing predictions back into a fifteen-year-old ERP is integration work, not ML work. Add two to four weeks and scope it separately.
• No named business owner. The single largest silent delay. Decisions queue, scope drifts, and a twelve-week build quietly becomes a twenty-week one with the same statement of work.

How fast can you get a first straight answer?

Faster than the full build, and cheaply. A ten-day Sprint Zero, sold as the AI Discovery Sprint at $3,250 or ₹2,00,000 and credited against the next build, gives you the decision definition, a data feasibility verdict and a dated plan. A three-week ProofRun, the AI POC Sprint at $6,250 or ₹4,00,000, proves the hardest part of the model on your real data before you commit.

The full machine learning development service runs from $17,500 to $70,000, or ₹11,20,000 to ₹46,40,000, and that band maps closely to the eight-to-sixteen-week range. Every starting figure is published on the pricing page. We quote fixed price against a fixed date, which is only possible because scope is locked at the end of framing; the reasoning is in fixed price, fixed date.

What can run in parallel, and what cannot

Genuinely parallel

Integration design and model development overlap well. Once the target variable and output contract are agreed, a backend engineer can build the serving path, the write-back and the audit table while the model is still being tuned. Monitoring and drift alerting can also be built against a dummy model. On a twelve-week build this parallelism saves about three weeks.

Stubbornly serial

Nothing useful happens before framing is done, because the target variable determines which data you need. Shadow running cannot be compressed either. It takes the time it takes for enough real cases to accumulate, which for a weekly decision means four weeks, not four days. Teams that cut shadow mode short are the ones that roll back in month two.

The phase people forget

Error analysis by segment. Aggregate accuracy hides the fact that the model is excellent on the eighty per cent of cases you did not need help with and poor on the twenty per cent you did. Budget a week for it. Google's Rules of Machine Learning makes the same point in its guidance to start with a simple baseline and measure carefully before adding complexity.

When asking for a timeline is the wrong question

Sometimes the honest answer is that no schedule is meaningful yet, and we say so.

If you cannot name the decision the model changes, a timeline is theatre. "We want to use our data better" is not a decision. "We want to stop shipping to addresses that will return the parcel" is. Until you have the second kind of sentence, spend two weeks on ranking use cases by ROI instead of buying a build.

If the outcome you want to predict happens fewer than a few hundred times a year, machine learning is usually the wrong tool and a written rule is the right one. If a simple threshold gets you most of the value, take it. And if your data was only recently instrumented, the honest timeline includes six months of collecting before any model is worth training; how much data do you need covers where the floor sits.

What a real schedule looked like

An NBFC came to us to cut the manual effort in KYC and loan onboarding. Framing took under two weeks because the decision was unambiguous: extract and validate fields from identity and income documents, flag the ones a human must see. Data access took longer, because the documents were private and the models had to run inside their perimeter. Shadow running carried on for several weeks while operations staff corrected the output. The finished system is described in the KYC document intelligence case study.

The lesson from that engagement was ordinary: the weeks went where the constraints were, not where the interesting maths was. Nobody on either side spent a day arguing about model architecture, and everybody spent days on document variants, access approvals and what the operations team should do with a low-confidence extraction.

Checklist before the clock starts

• Write the decision in one sentence, including what changes when the prediction is right
• Name the person who signs off the success threshold
• Confirm historical outcomes exist and are retrievable, not just that the data exists
• List every system that must be read from and written to, with API status for each
• Get data access requests into your security team before kick-off, not during week one
• Agree the shadow-running period in weeks and who reviews the log
• Decide the retraining cadence and who owns it after handover
• Budget for the care plan from month one, not from the first incident

Related reading

Machine learning development services cost in 2026 puts numbers against each phase, MLOps for mid-size companies covers what happens after go-live, and why AI pilots never reach production explains the gap that most optimistic schedules fall into.

Ask a vendor which phase they expect to overrun and why; the ones who answer with data access rather than modelling are the ones who have shipped before.