You will get Custom Machine Learning Model
Project details
Your historical data knows things: which customers are about to leave, what demand looks like next quarter, which transactions don't add up. I build custom machine learning models that turn that history into predictions you can act on.
WHAT I'LL BUILD:
• A model designed around your business question — churn, sales forecasting, demand planning, lead scoring, fraud/anomaly detection, pricing, classification
• Proper data preparation and feature engineering (this is where accuracy actually comes from)
• Honest validation on held-out data — you see real-world performance, not training-set hype
• A clear accuracy report in business terms, plus prediction scripts your team can run
• On the Advanced tier: deployed API, monitoring, and automated retraining so the model stays accurate as your data evolves
WHY ME:
12+ years in machine learning and data engineering, including enterprise work in banking (fraud detection at scale). I run a B2B AI platform and ship models to production with the validation discipline that separates a working model from a demo.
WHAT I'LL BUILD:
• A model designed around your business question — churn, sales forecasting, demand planning, lead scoring, fraud/anomaly detection, pricing, classification
• Proper data preparation and feature engineering (this is where accuracy actually comes from)
• Honest validation on held-out data — you see real-world performance, not training-set hype
• A clear accuracy report in business terms, plus prediction scripts your team can run
• On the Advanced tier: deployed API, monitoring, and automated retraining so the model stays accurate as your data evolves
WHY ME:
12+ years in machine learning and data engineering, including enterprise work in banking (fraud detection at scale). I run a B2B AI platform and ship models to production with the validation discipline that separates a working model from a demo.
Machine Learning Tools
Amazon SageMaker, Apache Spark MLlib, Databricks Platform, MLflow, NumPy, OpenCV, Python, Python Scikit-Learn, PyTorch, SAS, scikit-learn, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$900
|
Standard
$2,700
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 9 days | 18 days | 30 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 1 | 5 | 7 |
Number of Scenarios | 1 | 1 | 1 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Model Variation
+$400Frequently asked questions
About Reza
AI Engineer | Agentic AI, RAG, LLM Fine-tuning | Python, Azure ML
Toronto, Canada - 2:55 am local time
At bluemouse.ai, I design, train, and deploy intelligent systems that handle real-world automation at scale. My focus: Agentic AI, RAG, fine-tuning, and computer vision applications that drive measurable business outcomes.
Technical Expertise:
✅ Agentic AI & Orchestration – Agent design, tool integration, multi-step reasoning, and production deployment
✅ RAG Systems – Vector databases, semantic search, context retrieval, and hybrid search strategies
✅ LLM Fine-tuning & Training – LoRA, QLoRA, full fine-tuning, and custom instruction optimization
✅ Prompt Engineering – Few-shot learning, chain-of-thought design, and systematic prompt optimization
✅ Computer Vision – Object detection, image classification, segmentation, and vision-language models
✅ Data Stack – SQL, Python, PySpark; Azure Databricks, Synapse; distributed computing
✅ ML Operations – Model evaluation, validation frameworks, deployment pipelines, monitoring
Whether building custom fine-tuned models, designing agentic workflows, or deploying computer vision systems, I deliver solutions grounded in both technical rigor and business value.
Explore bluemouse.ai – See agentic AI in action.
Steps for completing your project
After purchasing the project, send requirements so Reza can start the project.
Delivery time starts when Reza receives requirements from you.
Reza works on your project following the steps below.
Revisions may occur after the delivery date.
Problem & data review
We define the prediction target, success metric, and I assess whether your data can support it (honestly).
Data preparation & feature engineering
I clean, structure, and engineer the features that drive model accuracy.