You will get a custom machine learning model built and delivered in Python
Project details
You will get a custom machine learning model trained on your data and ready for production use.
I build PyTorch models and NLP pipelines professionally — not notebook experiments.
Every deliverable includes data preparation, model training with hyperparameter tuning, an evaluation report with clear performance metrics, and a Python inference script you can integrate into your systems.
Whether you need prediction, classification, or forecasting, I will define the problem clearly with you upfront, select the right approach, and deliver a model that works.
Fixed-price, documented, and built to last.
I build PyTorch models and NLP pipelines professionally — not notebook experiments.
Every deliverable includes data preparation, model training with hyperparameter tuning, an evaluation report with clear performance metrics, and a Python inference script you can integrate into your systems.
Whether you need prediction, classification, or forecasting, I will define the problem clearly with you upfront, select the right approach, and deliver a model that works.
Fixed-price, documented, and built to last.
Machine Learning Tools
NLTK, NumPy, pandas, Python, Python Scikit-Learn, PyTorchWhat's included
| Service Tiers |
Starter
$800
|
Standard
$1,500
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | ||
Number of Graphs/Charts | 2 | 4 | 6 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$100
Additional Model Variation
(+ 3 Days)
+$300
Additional Graph/Chart
+$50Frequently asked questions
About Adam
Senior Python/AWS Engineer | Data Pipelines, ML Systems, API Dev
Epsom, United Kingdom - 4:05 am local time
With 20+ years of technology leadership including CTO roles at VC-backed start-ups, I bring architectural thinking to implementation work. You get senior-level code quality, clean documentation, and systems designed for production from day one — not prototypes that need rebuilding.
What I deliver well and quickly:
DATA ENGINEERING & ETL
S3, Glue, Athena, Lambda, RDS — I build and optimise serverless data pipelines daily. Recent work includes automated partition management, dimension table integration from Postgres, and processing 80M+ monthly event records through Glue/Athena analytics stacks.
ML & AI SYSTEMS
PyTorch model development, NLP pipelines (content classification, entity extraction, sentiment analysis), RAG architectures, and multi-head neural networks for prediction systems. I build end-to-end: data preparation, model training, evaluation, and deployment.
API & BACKEND DEVELOPMENT
FastAPI and Flask backends, REST API design, payment integrations, authentication systems, and serverless architectures on Lambda + API Gateway.
How I work:
- Fixed-price preferred — I scope carefully, price fairly, and deliver on time. Clean, - functional Python — minimal main(), well-structured libraries, comprehensive docstrings. - AWS-native — I default to serverless where it makes sense (Lambda, Glue, Athena, S3). - Production-grade — tests, error handling, logging, and documentation included as standard.
- Clear communication — you'll know exactly where things stand, no surprises.
Background:
- CTO at VC-backed startup (Series B, $25M raise)
- Head of Advertising Technology at a major broadcaster (led 125+ person team)
- Deep Learning certifications (PyTorch, computer vision)
- Harvard Business School certificates (Entrepreneurship, Negotiation)
- Based near London, UK — responsive across GMT and EST working hours
Steps for completing your project
After purchasing the project, send requirements so Adam can start the project.
Delivery time starts when Adam receives requirements from you.
Adam works on your project following the steps below.
Revisions may occur after the delivery date.
Define Problem
Review dataset, agree prediction target, define success metrics and constraints.
Data Preparation
Clean data, handle missing values, engineer features, and run exploratory analysis.