You will get a predictive maintenance ML system for your industrial sensor data
Project details
I built predictive maintenance systems in production for automotive and construction domains improved accuracy by 25% and eliminated 50+ hours of manual work per month. This is not a tutorial project. It is production experience.
Most ML freelancers have never worked with real industrial sensor data. I have — seismic drift sensors, brake analytics, multi-domain IoT streams. I know how noisy and inconsistent real sensor data looks and how to build models that hold up outside a notebook.
What you get: an anomaly detection model trained on your sensor data, automated retraining pipeline with Prefect orchestration, and a Dockerized FastAPI endpoint your team can query in real time. Every tier includes full source code.
Best for: manufacturers, IoT startups, industrial automation companies, and operations teams who need ML that actually runs in production not a demo.
Most ML freelancers have never worked with real industrial sensor data. I have — seismic drift sensors, brake analytics, multi-domain IoT streams. I know how noisy and inconsistent real sensor data looks and how to build models that hold up outside a notebook.
What you get: an anomaly detection model trained on your sensor data, automated retraining pipeline with Prefect orchestration, and a Dockerized FastAPI endpoint your team can query in real time. Every tier includes full source code.
Best for: manufacturers, IoT startups, industrial automation companies, and operations teams who need ML that actually runs in production not a demo.
Machine Learning Tools
Amazon SageMaker, Apache Spark, Azure Machine Learning, ChatGPT, Databricks MLflow, Google AutoML, Google Sheets, GPT-3, Keras, MATLAB, Microsoft Excel, MLflow, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, R, scikit-learn, Tableau, TensorFlow, Vertex AI, XGBoostWhat's included
| Service Tiers |
Starter
$200
|
Standard
$450
|
Advanced
$850
|
|---|---|---|---|
| Delivery Time | 6 days | 10 days | 16 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 4 |
Number of Graphs/Charts | 0 | 2 | 4 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Scenario
(+ 3 Days)
+$100Frequently asked questions
About Ikram Ullah
AI Engineer | Conversational AI | RAG & Workflow Automation
Mianwali, Pakistan - 3:46 am local time
👋 About Me
I am Ikram Ullah Khan, an Applied AI and Machine Learning Engineer with experience in production AI systems, industrial analytics, and MLOps. I build and deploy real-world machine learning solutions that automate processes, improve decision-making, and deliver measurable business impact, not just prototypes or demos.
I have worked on industrial AI systems, predictive maintenance pipelines, and automation workflows across engineering and data-driven environments, taking models from idea to deployment.
🛠️ What I Can Help You With
🔧 Production Machine Learning
• Predictive analytics and forecasting models
• Classification and regression systems
• Sensor and industrial data modeling
• Feature engineering and ML data pipelines
⚙️ MLOps and Deployment
• End-to-end ML pipelines using Prefect and CI/CD
• Dockerized ML environments
• Model deployment with FastAPI
• GitHub-based automation workflows
🤖 AI and NLP Automation
• Text classification and document processing
• GPT-powered workflows and automation
• AI-powered reporting and analytics tools
🏭 Industrial and Engineering AI
• Predictive maintenance systems
• Simulation-driven ML models
• Construction and automotive data analytics
• Process automation and digitalization
☁️ Tools and Stack
Python, Scikit-learn, TensorFlow, PyTorch, OpenCV
FastAPI, Docker, Prefect, GitHub CI/CD
SQL / NoSQL, AWS Lambda, API Gateway
🚀 Selected Work
• Built simulation-driven ML pipelines for predictive maintenance improving accuracy by 25 percent
• Automated ML workflows reducing manual effort by 50 plus hours per month
• Deployed containerized ML systems with CI/CD for production environments
• Developed AI APIs serving real-time predictions for business applications
• Designed industrial data pipelines for engineering analytics and automation
🤝 How I Work
• Clear scope and realistic timelines
• Production-focused solutions not academic demos
• Clean, scalable, maintainable code
• Transparent communication and regular updates
• Ownership from problem definition to deployment
✅ Best Fit Clients
• Startups building AI-driven products
• Companies automating operations with ML
• Teams needing production-ready AI systems
• Businesses working with sensor or engineering data
📬 Let us Build Something Practical
If you are looking for someone who can design, build, and deploy AI systems that actually work in production, send me a message. I will help you define the right technical approach for your project.
Steps for completing your project
After purchasing the project, send requirements so Ikram Ullah can start the project.
Delivery time starts when Ikram Ullah receives requirements from you.
Ikram Ullah works on your project following the steps below.
Revisions may occur after the delivery date.
Data Review & Scoping
I review your sensor data, identify failure patterns, and confirm the model approach before writing any code.
Feature Engineering & Model Training
Extract time-series features from sensor readings, train and validate the anomaly detection model, share results for review.