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You will get build and deploy a custom machine learning model for your business
Project details
I will build a custom machine learning model that helps you turn raw data into accurate predictions, classifications, forecasts, recommendations, or business insights. This project can support use cases such as customer behavior prediction, sales forecasting, anomaly detection, text classification, recommendation systems, and predictive analytics.
Depending on your package, I can help with data preprocessing, feature engineering, model training, evaluation, performance tuning, clean source code, API development, deployment guidance, and documentation. I work with Python, Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, SQL, FastAPI, and related ML tools.
My goal is to deliver a practical machine learning solution that is not just a notebook experiment, but a clear, usable, and business-focused model you can test, understand, and build on.
Depending on your package, I can help with data preprocessing, feature engineering, model training, evaluation, performance tuning, clean source code, API development, deployment guidance, and documentation. I work with Python, Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, SQL, FastAPI, and related ML tools.
My goal is to deliver a practical machine learning solution that is not just a notebook experiment, but a clear, usable, and business-focused model you can test, understand, and build on.
Machine Learning Tools
Amazon SageMaker, Apache MXNet, BERT, ChatGPT, Databricks MLflow, Deeplearning4j, GitHub Copilot, GPT-3, Keras, MATLAB, Microsoft CNTK, Microsoft Power BI, Minitab, NLTK, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, R, scikit-learn, Scrapy, SQL, XGBoostWhat's included
| Service Tiers |
Starter
$250
|
Standard
$500
|
Advanced
$800
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 14 days |
Number of Revisions | 2 | 4 | 6 |
Number of Model Variations | 2 | 4 | 8 |
Number of Scenarios | 2 | 6 | 12 |
Number of Graphs/Charts | 2 | 6 | 12 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$50 - $300Frequently asked questions
About Farzana
AI & Machine Learning Engineer | LLMs, RAG, Prompt Engineering, MLOps
Gilgit, Pakistan - 3:10 am local time
I help businesses build intelligent systems that work — at scale, in production, and with measurable results.
With 3+ years of hands-on experience as an ML and AI Engineer, I specialize in:
✅ Machine Learning & Predictive Modeling — Building and deploying ML models using Python, TensorFlow, Scikit-learn, and PyTorch for regression, classification, forecasting, and recommendation systems.
✅ Generative AI & LLMs — Developing RAG pipelines, AI chatbots, and custom LLM applications using OpenAI GPT, LangChain, and Hugging Face Transformers. Fine-tuning models for domain-specific tasks.
✅ NLP & Text Analytics — Sentiment analysis, topic modeling, text classification, named entity recognition (NER), and document processing pipelines.
✅ AI Engineering & MLOps — End-to-end AI system design, REST API development with FastAPI/Flask, model deployment on AWS/Azure/GCP, and CI/CD for ML pipelines.
✅ Prompt Engineering — Crafting optimized prompts for GPT-4, Claude, and other LLMs to maximize accuracy, relevance, and brand alignment for business applications.
✅ Computer Vision — Object detection (YOLO), image segmentation, OCR, and real-time video analytics systems.
✅ Data Analytics & Visualization — Power BI dashboards, SQL-based data pipelines, and actionable business intelligence reports.
Tech Stack: Python | TensorFlow | PyTorch | Scikit-learn | LangChain | OpenAI API | Hugging Face | FastAPI | Flask | AWS | Azure | SQL | Power BI | Docker
I hold a PhD in Data Science (University of Canterbury) and an MPhil in Computer Science (Quaid-e-Azam University), plus certifications from DeepLearning.AI and AWS.
Whether you need an ML model built from scratch, an AI chatbot integrated into your product, or a full generative AI pipeline — I deliver production-ready solutions, not just experiments.
Let's build something intelligent together.
Steps for completing your project
After purchasing the project, send requirements so Farzana can start the project.
Delivery time starts when Farzana receives requirements from you.
Farzana works on your project following the steps below.
Revisions may occur after the delivery date.
Review requirements and dataset
I will review your dataset, business goal, target output, data quality, and success metric to confirm the best ML approach.
Prepare and analyze the data
I will clean the data, handle missing values, explore patterns, prepare features, and structure the dataset for model training.