Building practical ML systems • Data Analytics • Deep Learning • MLOps • Python
I'm an AI Engineer focused on building practical machine learning systems and turning raw data into clear, useful decisions. I work across the full pipeline: analysis, feature engineering, model training, evaluation, deployment, and continuous improvement.
I care about models that work beyond notebooks — clean experiments, reliable pipelines, readable results, and production-minded thinking.
const codehub001 = {
location: "India",
role: ["AI Engineer", "Machine Learning & Deep Learning Practitioner", "MLOps Enthusiast"],
currentFocus: [
"Face Recognition using ML & Deep Learning",
"Model evaluation and deployment",
"Structured DSA practice"
],
learning: ["CRISP-DM", "SEMMA", "OSEM", "TDSP", "DataOps"],
openTo: ["AI/ML collaboration", "Data Analytics projects", "Computer Vision work"]
};| Focus | Details |
|---|---|
| Building | Face Recognition using ML & Deep Learning |
| Learning | CRISP-DM, SEMMA, OSEM, TDSP, DataOps |
| Practicing | DSA Challenge Sheet |
| Writing | ML systems and project lessons on Hashnode |
| Open to | Collaboration in AI/ML, Data Analytics, and Computer Vision projects |
- More posts coming soon at hashnode.com/@codehub01
Pandas · NumPy · Matplotlib · Seaborn · Tableau · Power BI
Data Analysis & Engineering Feature engineering, data cleaning, exploratory analysis, data wrangling, and pipeline design.
Machine Learning Classical ML algorithms, model selection, validation, tuning, and explainability.
Deep Learning Neural network workflows for computer vision and language-focused experiments.
MLOps & Deployment Reproducible workflows, model packaging, cloud-based deployment, and production-minded monitoring.