Custom models, trained on your data
Hire an AI model training developer who builds production-ready models
Most AI projects fail not because the idea is wrong, but because the model never makes it to production. I build, train, and deploy custom AI models on your actual business data: models that hold accuracy in the real world, integrate with your stack, and keep improving over time.
Available now, projects start within 48 hours, NDA before any data moves
Plain answer
What is AI model training?
AI model training is the process of teaching a machine learning system to make accurate predictions by exposing it to labelled or structured data and iteratively adjusting its internal parameters to minimise errors. A model trained on your domain-specific data consistently outperforms any generic pre-trained alternative for tasks like fraud detection, demand forecasting, document classification, or medical image analysis.
Training starts with data: collecting, cleaning, and splitting it into training, validation, and test sets. The right architecture is selected based on the task, a transformer for text, a CNN for images, gradient boosting for tabular data. The model iterates over the data, computes a loss function, and updates weights via backpropagation using optimisers such as Adam or AdamW. Post-training, the model is evaluated on held-out data using metrics like F1, AUC-ROC, or RMSE, then tuned until it meets production accuracy targets. Custom AI model training is the difference between a model built for everyone and a model built for your data.
The strongest argument on this page
Why custom AI model training beats off-the-shelf solutions
Generic pre-trained models are built on public datasets. Your business data is different: your customers, products, terminology, and patterns are unique. Fine-tuned or fully custom-trained models consistently outperform generic alternatives on domain-specific tasks.
| Factor | Custom trained model | Pre-trained API (GPT etc.) | Off-the-shelf ML tool |
|---|---|---|---|
| Accuracy on your data | Highest, trained on domain data | Moderate, generic training | Low to moderate |
| Data privacy | Full control, on-prem or private cloud | Data sent to a third party | Varies |
| Cost at scale | Low, one-time training cost | High, per-token pricing | Moderate, licence fees |
| Customisation | Unlimited | Prompt engineering only | Limited configuration |
| Latency | Low, self-hosted | API round-trip latency | Varies |
| Ownership | You own the model | No ownership | Licence only |
Freelance vs agency vs in-house
A freelance AI model training developer gives you direct access to the engineer doing the build, faster iteration, and lower overhead than an agency. No 3 to 6 month in-house hiring cycle, no salary overhead. Engagements start in 48 hours.
Run the numbers first
Custom trained model vs pre-trained API, at your volume
The "cost at scale" row above, turned into a number. Per-token pricing looks cheap at a demo. It stops looking cheap once volume shows up.
Estimate only, driven by the assumptions above. At low inference volume, a pre-trained API can still be the cheaper and faster path, and I will say so on the discovery call if the numbers point that way. This tool exists to show the crossover point, not to talk you into training a model you do not need.
What I build
AI model training services
Custom machine learning models that turn data into smart solutions, from data preparation and training through to deployment. Amber tags are the layers most projects underestimate.
Custom ML model development
Machine learning architectures matched to your data type and task: supervised, unsupervised, or semi-supervised. Output is a trained, validated, production-ready model with an inference API.
- Classification, regression, and ranking models
- Domain-specific architecture design
- ONNX / TorchScript export for low-latency inference
Deep learning and neural network training
For tasks requiring deep feature extraction: image recognition, speech processing, time-series forecasting, and multi-modal inputs, tailored to your dataset size and compute budget.
- CNNs for image and video tasks
- Transformers for sequence and text tasks
- LSTM / GRU for time-series and signal data
LLM fine-tuning on private data
Fine-tune LLaMA 3, Mistral, Phi-3, or Gemma on your proprietary documents, support tickets, contracts, or domain knowledge, using QLoRA and LoRA for cost-efficient GPU training.
- QLoRA / LoRA fine-tuning
- Instruction-following and task-specific alignment
- RLHF-style preference tuning
Data preparation and feature engineering
Model accuracy is constrained by data quality. This step alone can lift model accuracy by 10 to 20% before a single training epoch.
- Automated data cleaning pipelines
- Feature selection and dimensionality reduction
- Class imbalance handling: SMOTE, weighted sampling
Hyperparameter optimisation
Systematic search over the model configuration space using Bayesian optimisation, Optuna, or grid and random search, the exact search shown in the demo above.
- Bayesian optimisation with Optuna
- Multi-objective optimisation: accuracy vs latency
- Automated experiment tracking with MLflow
Model evaluation and accuracy improvement
Rigorous evaluation on held-out test sets with interpretability analysis to explain predictions, identify failure modes, and address bias.
- Confusion matrix, F1, AUC-ROC, RMSE benchmarks
- Explainability reports: SHAP values
- Bias detection and mitigation
Transfer learning and domain adaptation
Start from a strong pre-trained base such as BERT, ResNet, EfficientNet, or Whisper, and adapt it to your domain with a fraction of the data and compute cost of training from scratch.
- Domain-adaptive fine-tuning
- Few-shot and zero-shot learning setups
- Knowledge distillation for smaller deployable models
Model deployment, monitoring, and retraining
A model in production degrades as data shifts. I deploy models as versioned REST APIs, set up drift detection, and build automated retraining pipelines that keep accuracy stable.
- FastAPI + Docker containerised deployment
- MLflow model registry and versioning
- Automated drift alerts and retraining triggers
Not sure which one you need?
A 30-minute discovery call clarifies scope before anything is billed.
Talk it throughThe architecture behind the demo above
Bayesian optimisation vs random search
Every model has a configuration space: learning rate, batch size, network depth, regularisation strength, and more. Grid and random search test configurations without learning from earlier results. Bayesian optimisation builds a probabilistic model of which regions of that space are promising, and spends more of its budget there, converging on a strong configuration in far fewer trials for the same compute spend. It is a search heuristic, not a guarantee: it can still miss the true global optimum, which is why the result is still validated against held-out data before anything ships.
When every trial is expensive
- Deep learning models where a single training run takes hours on GPU
- Large search spaces with many interacting hyperparameters
- A fixed, limited compute or time budget for the search itself
When trials are cheap and the space is small
- Lightweight models such as gradient boosting on tabular data
- A small number of hyperparameters worth testing exhaustively
- Early exploration, before committing to a more targeted search
Both strategies use the same underlying infrastructure: an experiment tracker, a scoring function, and a compute budget. Which one is worth setting up depends entirely on how expensive your model is to train, and that is exactly what the discovery call figures out before any GPU time is spent.
Where it runs
Industries and use cases
Domain-adapted training experience across these industries means faster time-to-accuracy for your project.
Healthcare
Medical image classification, clinical note extraction, ICD code prediction, patient readmission risk scoring.
Fintech
Fraud detection models, credit scoring, transaction anomaly detection, earnings call sentiment analysis.
Ecommerce and retail
Demand forecasting, product recommendation models, returns prediction, and visual search.
SaaS and B2B
Churn prediction from usage signals, support ticket priority scoring, and lead scoring models.
Manufacturing
Predictive maintenance from sensor data, defect detection in production images, and yield optimisation.
HR and recruiting
Resume-to-job matching models, candidate success prediction, workforce demand forecasting.
Your industry not listed?
The training approach transfers. The data source is what changes.
Talk it throughPricing
What it costs to hire an AI model training developer
All engagements start with a free 30-minute discovery call and a written spec before any billing.
Proof of concept
A baseline model plus an evaluation report. The cheapest way to find out whether your data supports the target.
Full production
Data preparation, training, optimisation, deployment, and monitoring, in one scoped engagement.
Dedicated training engineer
Set weekly hours for an ongoing model roadmap. Same standing offer as my other AI service lines.
Hourly consulting
Architecture reviews and model audits for senior ML engineers. Useful before committing to a build.
How it gets built
How I build high-performance AI models
I start by understanding the problem and the data behind it, then design and train models that solve real business challenges. Practical, efficient, and ready to use in real environments.
PHASE 01days 1 to 2Discovery
+
PHASE 02the decisive oneData audit and preparation
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PHASE 0348 hours inArchitecture selection and baseline
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PHASE 04iterated to targetTraining, optimisation, and evaluation
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PHASE 05handoffDeployment and integration
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PHASE 0630 days plusMonitoring and ongoing support
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Tooling
Technologies powering custom AI models
A carefully selected AI and machine learning stack to design, train, and optimise custom-built models. From experimentation and fine-tuning to scalable deployment, these tools support precision, performance, and long-term reliability.
How the work actually flows
Intelligent model training for real-world impact
From preparing data to training and validating models, the focus is on building practical AI solutions that deliver meaningful and measurable results.
Data collection & feature engineering
Analyze, clean, and structure raw data while engineering features that improve accuracy and stability.
Custom architecture design
Bespoke model architectures using TensorFlow, PyTorch, and Keras, optimised for performance and scale.
Iterative training & optimisation
Continuous experimentation and hyperparameter tuning to achieve superior accuracy and reliability.
Validation & evaluation
Rigorous testing ensures model robustness, bias reduction, and real-world operational fit.
Deployment & continuous learning
Production monitoring pipelines enable continuous learning and future scalability.
Features
Why work with Shreyans Padmani
Training AI models with your data to deliver accurate results that perform reliably in real-world applications.
Custom data training
Every project starts with understanding your data. I prepare and train models using your datasets to ensure the system learns patterns that match your real business needs.
Model optimisation
I fine-tune trained models to improve accuracy, reduce errors, and ensure stable performance across different scenarios.
Deployment-ready models
Trained models are delivered in a format that can be easily deployed into your existing systems, making them ready for real-world use.
Delivered work
Deep expertise in advanced AI technologies
Three delivered projects with the numbers that actually shipped.
AI resume screening: custom NER plus ranking model on 10,000+ resumes
A hiring platform needed automated candidate shortlisting. I trained a custom NER model for skills, education, and experience extraction, and a ranking model for job-fit scoring. Deployed on AWS, processing 10,000+ PDFs per month.
AI video intelligence: Whisper plus extractive NLP summarisation pipeline
A training and enablement platform needed automatic meeting and video summaries. I built an end-to-end pipeline: Whisper for transcription, a custom extractive NLP model trained on meeting corpora for summarisation.
AI customer feedback classifier: 14-class BERT fine-tune on 8,000 labels
Manual survey analysis took 3 days per reporting cycle. I fine-tuned a BERT model on 8,000 labelled open-text feedback records across 14 business categories.
About
Freelance AI model training developer
I am an independent AI engineer with 5+ years building and deploying custom machine learning models for companies in SaaS, healthcare, fintech, manufacturing, and ecommerce. My work spans the full model lifecycle: data audit, architecture design, training, evaluation, deployment, and monitoring.
I work directly with every client. No account managers, no junior developers doing the actual build. When you hire me, the person who scopes your project is the same person who trains your model and ships your API.
FAQ
