Machine Learning That Survives Past the Proof of Concept
Forecasting, classification, computer vision and recommendation systems built with the monitoring, retraining and evaluation pipelines that decide whether a model is still useful in month eight.
What do AI and machine learning services include?
AI and machine learning services cover problem framing, data assessment and preparation, feature engineering, model selection and training, evaluation against a business metric, deployment as an API or batch job, and the MLOps work that keeps a model reliable in production: monitoring, drift detection, retraining and versioning. Anviam delivers all of these, and starts every engagement with a data readiness assessment because insufficient or poorly labelled data is the most common reason ML projects fail.
- Data assessment
- 1–2 weeks, written findings
- Pilot model
- 4–8 weeks to measurable baseline
- Production ML
- 10–16 weeks including MLOps
- Stack
- Python, PyTorch, scikit-learn, MLflow
- Deployment
- Real-time API or scheduled batch
- Included
- Drift monitoring and retraining pipeline
Most Failed ML Projects Were Data Projects in Disguise
The model is rarely the hard part. Modern libraries make training straightforward, and for most business problems a well-tuned gradient boosting model beats something exotic. What sinks projects is data: not enough history, labels that were never captured, features that leak information from the future, or a training set drawn from conditions that no longer hold. None of that is visible in a notebook that reports 94 percent accuracy.
So we begin with a data readiness assessment before proposing a model. How much labelled history exists, how it was collected, what the class balance looks like, whether the features will be available at prediction time, and what the realistic performance ceiling is. Occasionally the honest conclusion is that you need six months of instrumentation before ML is worth attempting, and we would rather say that than bill for a model that cannot work.
Machine Learning Services We Deliver
Forecasting & Predictive Models
Demand, churn, risk and capacity forecasting with proper backtesting against held-out time periods.
Computer Vision
Detection, classification and OCR for inspection, document processing and quality control workflows.
NLP & Document Intelligence
Classification, entity extraction and summarisation over contracts, tickets, claims and clinical notes.
Recommendation Systems
Personalisation and ranking models with the cold-start and diversity handling that make them usable.
Anomaly & Fraud Detection
Outlier detection on transactions, telemetry and access patterns, tuned for a workable false-positive rate.
MLOps & Model Platforms
Training pipelines, model registries, drift monitoring and automated retraining with rollback.
The Machine Learning Stack We Work In
Where We Have Applied Machine Learning
Clinical Risk Scoring
Readmission and deterioration risk models built with clinician review and explainability requirements.
Demand & Route Forecasting
Delivery volume and routing prediction, in the domain our Tankspotter dispatch platform operates in.
Credit & Fraud Models
Scoring and anomaly detection with the audit trail and adverse-action explainability regulators expect.
Predictive Maintenance
Failure prediction from equipment telemetry, tied to maintenance scheduling rather than dashboards.
Retail Personalisation
Product ranking and recommendation with measurable revenue-per-session testing.
Document Processing
Extraction from invoices, claims and forms, with confidence thresholds routing exceptions to humans.
What We Do Differently on ML Projects
The gap between a demo model and a production model is mostly engineering discipline. These are the practices we apply as standard.
Talk to Our Team- A written data readiness assessment before any model is proposed, including when the answer is no
- A trivial baseline model first, so added complexity has to earn its place on measured results
- Evaluation on a time-based holdout, not a random split, for anything with a temporal dimension
- Explicit leakage checks on every feature, confirming it is available at real prediction time
- Drift monitoring and alerting shipped with the model rather than promised as a later phase
- Model cards documenting training data, limitations and known failure modes for audit and handover
How an ML Engagement Runs
Problem Framing
We convert the business question into a prediction target and agree the metric success will be measured on.
Data Readiness Assessment
Volume, quality, labelling and leakage checks, with an honest verdict on feasibility.
Baseline Then Iterate
A simple model first to establish the floor, so later complexity has to justify itself.
Deploy With Guardrails
Serving infrastructure, confidence thresholds, human review paths and fallback behaviour.
Monitor & Retrain
Drift detection and scheduled retraining, because model quality decays quietly rather than failing loudly.
Common Questions About AI & Machine Learning Services
How much data do we need for machine learning?
It depends on the problem far more than on a headline number. Tabular classification with strong signal can work with a few thousand labelled examples; image classification with transfer learning often needs a few hundred per class; time-series forecasting needs enough history to cover at least two full seasonal cycles. The more useful question is how many labelled positive examples you have for the rare event you care about, since that is usually the binding constraint. Our data assessment answers this specifically for your case.
What is the difference between machine learning and generative AI?
Traditional machine learning predicts a value or category from structured or observed data: will this customer churn, is this transaction fraudulent, what demand should we expect. Generative AI produces new content — text, code, images — usually via a large pre-trained model you adapt rather than train. They suit different problems and are often combined, for example an ML model scoring risk while an LLM drafts the explanation. If your problem is prediction from your own historical data, that is ML.
Can you work with our existing data warehouse?
Yes. We build against Snowflake, BigQuery, Redshift, Databricks and plain PostgreSQL, and we prefer to read from your warehouse rather than create a parallel data path that immediately diverges. Where the warehouse lacks the granularity or history a model needs, we say so in the assessment and scope the instrumentation separately rather than quietly training on inadequate data.
How do you stop a model degrading after launch?
By assuming it will and instrumenting for it. We monitor input distributions for drift against the training set, track prediction distributions for sudden shifts, and where ground truth arrives later we measure live accuracy on a rolling basis. Alerts fire on thresholds you agree, and a retraining pipeline can be scheduled or triggered. Models are versioned with the training data snapshot so a rollback is a deployment rather than an archaeology exercise.
Do you provide explainability for regulated use cases?
Yes, and for regulated decisions we usually recommend model choices that are inherently more interpretable rather than post-hoc explanations of a black box. Where a complex model is justified we provide SHAP-based feature attribution, decision-level reason codes, and a model card documenting training data, performance by segment and known limitations. For credit and clinical work we also design in the human review path from the start.
Can you deploy models on our own infrastructure?
Yes. Deployment can be to AWS SageMaker, a containerised service on your Kubernetes cluster, a serverless endpoint, or a scheduled batch job writing predictions back to your warehouse. For clients with data residency or air-gap requirements we deploy entirely within your environment using open-weight models where a hosted API would otherwise be involved.