You will get XAI Reports using SHAP LIME and GradCAM for Model Transparency
Rising Talent
Project details
Unlock the "Black Box" of your Machine Learning Models with advanced XAI techniques.
In today's AI landscape, high accuracy isn't enough you need to understand why your model makes specific predictions. Whether you need to build trust with stakeholders, debug a failing model, or ensure regulatory compliance, I provide the transparency you need.
I specialize in three core pillars of Explainable AI:
SHAP (SHapley Additive exPlanations): I will provide global feature importance plots and dependence plots to show exactly which variables drive your model's decisions.
LIME (Local Interpretable Model-agnostic Explanations): I will analyze specific instances (predictions) to explain individual "what-if" scenarios.
Grad-CAM: For Computer Vision and Deep Learning (CNNs), I will generate heatmaps that visualize exactly where your model is "looking" in an image.
What you get:
Clean, commented Python code (Jupyter Notebooks).
High-resolution visualizations ready for reports or presentations.
Actionable insights into your model's behavior.
Stop guessing. Let's make your AI transparent, interpretable, and trustworthy.
In today's AI landscape, high accuracy isn't enough you need to understand why your model makes specific predictions. Whether you need to build trust with stakeholders, debug a failing model, or ensure regulatory compliance, I provide the transparency you need.
I specialize in three core pillars of Explainable AI:
SHAP (SHapley Additive exPlanations): I will provide global feature importance plots and dependence plots to show exactly which variables drive your model's decisions.
LIME (Local Interpretable Model-agnostic Explanations): I will analyze specific instances (predictions) to explain individual "what-if" scenarios.
Grad-CAM: For Computer Vision and Deep Learning (CNNs), I will generate heatmaps that visualize exactly where your model is "looking" in an image.
What you get:
Clean, commented Python code (Jupyter Notebooks).
High-resolution visualizations ready for reports or presentations.
Actionable insights into your model's behavior.
Stop guessing. Let's make your AI transparent, interpretable, and trustworthy.
AI Development Type
Deep Learning, Model TuningAI Tools
deeplearn.js, Keras, MLflow, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$50
|
Standard
$150
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | |
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10 - $100
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JF
Johan Henrik F.
Jul 15, 2026
Freelancers: honest review of an AI job-analysis tool (~30 min, $10)
Great and valuable feedback.
RS
Ramon S.
Apr 17, 2026
AI Engineer for Daily Widget Development
I would highly reccomend using Iqra for your projects! She was fast and professional.
About Iqra
Senior AI Solutions Architect | LLMs, CV, MLOps & Agentic Systems
100%
Job Success
Chiniot, Pakistan - 10:27 pm local time
1. What I Build
AI-powered MVPs and SaaS applications
LLM-powered applications and RAG systems
AI agents and intelligent automation workflows
Machine learning and deep learning solutions
NLP and computer vision applications
Recommendation systems
Healthcare AI applications
AI APIs and full-stack AI products
Production deployment and MLOps
2. How I Work
With a BS in Artificial Intelligence, I’ve built my foundation across machine learning, deep learning, NLP, computer vision, reinforcement learning, recommendation systems, generative AI, and MLOps. I don’t see AI as simply plugging an API into an app. I start with the problem, understand what actually needs to be solved, choose the right approach, build and evaluate the AI system, and then turn it into something that works reliably in a real application.
What I Can Handle
• AI/ML Development: Building, training, evaluating, and improving machine learning and deep learning models
• Data & Model Pipeline: Data preprocessing, feature engineering, model selection, training, and evaluation
• LLM Applications: LLM integration, RAG pipelines, embeddings, vector search, and context-aware applications
• AI Agents: Agent workflows, tool integration, reasoning pipelines, and task automation
• NLP & Computer Vision: Building AI systems for language, image, and real-world visual problems
• Model Optimization: Improving model performance, accuracy, reliability, and inference efficiency
• AI Application Development: Turning AI models into usable applications through APIs and backend systems
• Deployment & MLOps: Docker, Kubernetes, model deployment, ML pipelines, monitoring, and production workflows
• End-to-End AI Systems: Connecting the data, model, backend, application, and deployment into one working system
AI Foundation
My foundation comes from a BS in Artificial Intelligence, where I studied AI from both the theoretical and practical side. This includes understanding how models work, how to choose and evaluate the right approach for a problem, and how to take those concepts into real applications. My academic work has covered areas ranging from machine learning and deep learning to NLP, computer vision, reinforcement learning, recommendation systems, knowledge representation and reasoning, healthcare AI, and MLOps.
Selected AI Work
Skin Cancer Detection: Deep learning based medical imaging system with explainability
AI Recommendation System: Personalized recommendations using machine learning
Healthcare AI: AI solutions for medical image analysis and clinical applications
AI Report Matching: Semantic matching and intelligent retrieval for complex report requirements
AI Agents & RAG: LLM based applications with retrieval and intelligent workflows
Steps for completing your project
After purchasing the project, send requirements so Iqra can start the project.
Delivery time starts when Iqra receives requirements from you.
Iqra works on your project following the steps below.
Revisions may occur after the delivery date.
Model Loading & Data Preprocessing
I will load your trained model environment and format the sample dataset to ensure it is compatible with the selected XAI libraries.
Integration of XAI Libraries
I will configure and apply the appropriate explainability tool (SHAP for feature importance, LIME for local instances, or GradCAM for images).