You will get AI Deep Learning Expert | Hugging Face Model Training & Fine-Tuning
Project details
My project stands out because it combines deep expertise in Hugging Face Transformers, PyTorch, TensorFlow, and advanced deep learning techniques with a client-focused approach. I specialize in designing and fine-tuning state-of-the-art AI models for NLP, computer vision, and generative AI tasks, ensuring accuracy, scalability, and efficiency. What sets this apart is not just building models, but delivering production-ready, well-documented, and reproducible solutions tailored to your dataset and business goals. I provide clear communication, robust model validation, and hands-on optimization to maximize performance. From data preprocessing and feature engineering to fine-tuning large language models, deployment, and integration, I ensure your project achieves real-world impact. Whether you need a custom AI model trained from scratch, a Hugging Face pipeline optimized, or integration with existing systems, this service delivers end-to-end excellence with a balance of technical depth and business practicality.
Machine Learning Tools
BERT, ChatGPT, deeplearn.js, Deeplearning4j, GitHub Copilot, Google AutoML, Google Sheets, GPT-3, Keras, Kubeflow, Microsoft Excel, MLflow, NumPy, Open Neural Network Exchange, OpenCV, pandas, PyMC, Python, Python Scikit-Learn, R, Tableau, TensorFlowWhat's included
| Service Tiers |
Starter
$150
|
Standard
$350
|
Advanced
$650
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 12 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 0 | 2 | 3 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | |||
Source Code |
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Randy M.
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All I can say is Sameer is a must hire. I have hired him 10 times and keep coming back for more. When something is good, you don't change a thing. Thanks Sameer!
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All I can say is, Sameer is a good seller highly recommended. Sameer knows his trade; hire him you won't be sorry.
About Sameer
Data Scientist & Analyst | SQL, Python, Power BI, ML & Data Pipelines
69%
Job Success
Gilgit, Pakistan - 1:04 am local time
I'm a Data Scientist and Analyst who covers the full data lifecycle: data engineering, analytics, business intelligence, and dashboards — with machine learning and predictive analytics available when your project needs it.
WHAT I DELIVER FOR YOU:
- Data Analysis & Business Analysis: requirements gathering, KPI tracking, revenue & trend analysis, and clear stakeholder reporting
- Dashboards & Visualization: interactive Power BI, Tableau, and Looker Studio dashboards and executive-ready reports
- Data Engineering: clean, scalable ETL/ELT pipelines and data warehouse setup (Snowflake, BigQuery, Redshift) using Python & SQL
- Data Science & ML (when needed): predictive models, forecasting, churn analysis, and A/B testing
PROOF I DELIVER RESULTS:
1. 12 jobs completed with mostly 5-star reviews across analytics, Power BI, and data projects
2. One client hired me 10+ times and keeps coming back — repeat business built on reliable delivery
3. Delivered on-time Power BI dashboards, data preprocessing, and analytics projects with 5.0 ratings
4. Master's in Machine Learning + Microsoft Power BI and IBM certifications
TOOLS I WORK WITH:
SQL | Python | Power BI | Tableau | Looker Studio | Excel/Google Sheets | Pandas | PostgreSQL | MySQL | Snowflake | BigQuery | Apache Spark | Airflow
HOW WE'LL WORK:
We start with your goals and data, I map out a clear plan and deliverables, then I deliver clean, documented results with regular updates so you're never guessing.
Ready to turn your data into clear decisions? Send me a message with your project and let's get started.
Steps for completing your project
After purchasing the project, send requirements so Sameer can start the project.
Delivery time starts when Sameer receives requirements from you.
Sameer works on your project following the steps below.
Revisions may occur after the delivery date.
Step #1
Receive your dataset and project goals, perform exploratory data analysis, clean and preprocess the data (tokenization/embedding for text, normalization for numeric), create train/validation/test splits and prepare Hugging Face-compatible datasets.
Step #2
Select and configure the base model, set up training pipeline (Trainer/Accelerate or custom loop), run fine-tuning with hyperparameter tuning and checkpointing, monitor training metrics and iterate until validation performance is satisfactory.