Software Engineers Moving to ML
You have strong programming fundamentals and want to transition into data science or ML engineering — with a production-first mindset rather than a research-first approach.
Data Science Training (Architect-Led)
Go beyond Jupyter notebook experiments to building production ML systems. This training covers data engineering, feature engineering, model training, evaluation, and deployment — with the architectural thinking that separates notebook prototypes from systems that run reliably in production. You will learn to think about data pipelines, not just algorithms.
Most data science courses teach you to call scikit-learn fit and predict in a notebook and call it done. But production ML is a different discipline entirely. Your model needs to handle data drift, retrain on schedule, serve predictions at low latency, and fail gracefully when upstream data sources change format at 3 AM. This training teaches you the engineering side of data science — how to build reproducible pipelines, version your datasets and models, set up proper evaluation frameworks, and deploy models behind APIs that your team can actually depend on. You will work with real messy datasets, not cleaned CSV files, and build systems that a platform team would approve for production.
Who this training is for
Engineers ready to build ML systems, not just models.
Data Analysts Leveling Up
You are comfortable with SQL and basic Python but want to move beyond dashboards into predictive modeling, feature engineering, and machine learning pipelines.
ML Engineers Seeking Depth
You have built models but struggle with the full lifecycle — reproducible training, proper evaluation, model serving, monitoring, and handling data drift in production environments.
Tech Leads Overseeing ML Projects
You are managing data science teams and need to understand ML pipelines, infrastructure decisions, and the architectural patterns that separate prototype ML from production ML.
What you will learn
From raw data to production ML systems.
Python for Data Science (pandas, NumPy)
Master pandas DataFrames, NumPy array operations, and vectorized computation. Learn to write performant data transformations that handle millions of rows without running out of memory.
Exploratory Data Analysis & Visualization
Build intuition about datasets through systematic exploration. Use matplotlib, seaborn, and plotly to uncover distributions, correlations, outliers, and patterns that inform feature engineering decisions.
Feature Engineering & Data Pipelines
Design features that actually improve model performance. Build reproducible data pipelines that handle missing values, encode categorical variables, scale numerical features, and generate derived features consistently across training and serving.
Supervised Learning (Classification & Regression)
Implement decision trees, random forests, gradient boosting, and linear models with scikit-learn. Understand bias-variance trade-offs, hyperparameter tuning, and when each algorithm is the right choice for your problem.
Unsupervised Learning & Clustering
Apply K-means, DBSCAN, hierarchical clustering, and dimensionality reduction techniques. Learn to evaluate cluster quality, choose the right algorithm for your data shape, and communicate results to stakeholders.
Deep Learning Fundamentals (PyTorch/TensorFlow)
Build neural networks from scratch, then scale with PyTorch or TensorFlow. Cover CNNs for image data, RNNs and transformers for sequences, and understand when deep learning outperforms classical ML — and when it does not.
ML Model Evaluation & Validation
Go beyond accuracy. Learn precision, recall, F1, ROC-AUC, cross-validation, stratified sampling, and how to detect data leakage. Build evaluation frameworks that catch overfitting before your model reaches production.
Production ML Pipelines & MLOps
Deploy models behind REST APIs with FastAPI. Set up model versioning with MLflow, automate retraining pipelines, monitor for data drift, and implement A/B testing frameworks for model rollouts.
Real production projects
Build ML systems that solve actual business problems.
End-to-End ML Pipeline for Customer Churn Prediction
Build a complete churn prediction system from raw transactional data. Design features from behavioral signals, train and evaluate multiple models, deploy the best model behind an API, and set up monitoring to detect when prediction quality degrades over time.
pandas
scikit-learn
XGBoost
FastAPI
MLflow
Recommendation System with Production Deployment
Design and deploy a recommendation engine that handles cold-start problems, scales to millions of users, and serves personalized recommendations in under 100ms. Implement collaborative filtering, content-based approaches, and hybrid methods with proper A/B evaluation.
PyTorch
Redis
PostgreSQL
Docker
A/B Testing
Real-Time Anomaly Detection System
Build a streaming anomaly detection pipeline for infrastructure metrics. Process time-series data in real time, implement statistical and ML-based detection methods, handle seasonality and trend components, and deliver alerts with context that operations teams can act on.
Python
Kafka
TensorFlow
Grafana
Docker
Training format
Structured for real skill building.
Weekly 1:1 / Batch Sessions
60-90 minute live sessions focused on concept depth, code reviews, and hands-on problem solving. Not lectures — collaborative working sessions tailored to your pace.
Hands-On Assignments
Real implementation work between sessions. Build data pipelines, train models on messy datasets, deploy APIs, and debug issues that mirror production ML challenges.
Architecture Reviews
Submit your pipeline designs and model architectures for detailed review. Get feedback on data flow, feature engineering choices, evaluation methodology, and deployment strategy.
Ongoing Support
Async guidance between sessions via chat. Share dataset challenges, ask questions about model selection trade-offs, and get quick feedback on your implementation approach.
Your instructor
Learn from a practicing architect.
Vijaykumar NJ
Software Architect • 20+ Years Experience
- Designed production ML pipelines and data platforms for enterprise-scale applications
- Hands-on experience building end-to-end ML systems from data ingestion to model serving
- Led data engineering and ML infrastructure projects across e-commerce, compliance, and fintech domains
- Deep expertise in Python ecosystems, cloud-native ML deployment, and MLOps practices
- Active practitioner who builds data systems — not just someone who teaches about them
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