You will get End-to-End Dockerized ML & DL App: Linear, Logistic Regression, XGBoost
Project details
**Computer Vision:** High-accuracy model fine-tuning for tasks such as classification and object detection. I work extensively with **PyTorch and TensorFlow** to build and deploy efficient vision models.
**Machine Learning & Predictive Modeling:** I build robust models for structured data using **Linear Regression, Logistic Regression, and advanced ensemble methods like XGBoost, Random Forest, and AdaBoost**, enabling accurate forecasting, classification, and business intelligence applications.
**Time Series Forecasting:** I address forecasting challenges using the **Darts library** with powerful deep learning architectures such as **FFT, RNN, TCN, NBEATS, and DeepAR**.
### My End-to-End Technology Stack
• **Experiment Tracking:** MLflow
• **API Serving:** FastAPI
• **Containerization:** Docker
• **Model Optimization:** ONNX
• **Interactive Demos:** Streamlit & Gradio
• **Deployment:** Hugging Face Spaces
**Primary Framework:** My core development is done in **PyTorch and TensorFlow**, enabling scalable, modern AI solutions across **deep learning and classical machine learning workflows**.
Note on NLP: For NLP tasks, please check out my other specialized project.
**Machine Learning & Predictive Modeling:** I build robust models for structured data using **Linear Regression, Logistic Regression, and advanced ensemble methods like XGBoost, Random Forest, and AdaBoost**, enabling accurate forecasting, classification, and business intelligence applications.
**Time Series Forecasting:** I address forecasting challenges using the **Darts library** with powerful deep learning architectures such as **FFT, RNN, TCN, NBEATS, and DeepAR**.
### My End-to-End Technology Stack
• **Experiment Tracking:** MLflow
• **API Serving:** FastAPI
• **Containerization:** Docker
• **Model Optimization:** ONNX
• **Interactive Demos:** Streamlit & Gradio
• **Deployment:** Hugging Face Spaces
**Primary Framework:** My core development is done in **PyTorch and TensorFlow**, enabling scalable, modern AI solutions across **deep learning and classical machine learning workflows**.
Note on NLP: For NLP tasks, please check out my other specialized project.
Machine Learning Tools
Keras, MLflow, NLTK, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, Tableau, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$300
|
Standard
$650
|
Advanced
$800
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 2 | 3 | 5 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 2 | 3 | 5 |
Model Validation/Testing | |||
Model Documentation | - | - | |
Data Source Connectivity | |||
Source Code |
About MD Ashik
AI & DL Engineer | ML, CV, Gen AI, Agentic AI, NLP
Dhaka, Bangladesh - 8:19 am local time
I specialize in PyTorch and TensorFlow, delivering expert solutions in Computer Vision and Generative AI. My experience spans the full deep learning pipeline, from data preparation and fine-tuning to deployment.
My core expertise includes Deep Learning, Computer Vision, Generative and Agentic AI, and NLP with transformer-based models.
I deliver scalable, production-ready AI solutions tailored to your needs.
Steps for completing your project
After purchasing the project, send requirements so MD Ashik can start the project.
Delivery time starts when MD Ashik receives requirements from you.
MD Ashik works on your project following the steps below.
Revisions may occur after the delivery date.
Data Review & Preprocessing
I will analyze your dataset, handle missing values/outliers, and format the data (e.g., image resizing for CV or structuring for Time Series) to ensure it is perfectly prepared for model training.