Trainings
Short, intensive programs designed for real-world delivery: skills you can apply immediately.
Machine Learning Foundations (Python)
3 daysA practical, end-to-end introduction to supervised ML: data prep, modeling, evaluation, and production-ready baselines.
Deep Learning & Computer Vision (Practice)
4 daysTrain CNNs with strong discipline: augmentation, imbalance, transfer learning, and reliable training/debugging patterns.
GenAI: LLM Apps (RAG, Agents & Evaluation)
3 daysBuild reliable LLM applications: RAG pipelines, evaluation, safety, and agent workflows for real delivery constraints.
MLOps Starter: FastAPI + Docker + CI/CD
4 daysDeploy an ML model as an API, containerize it, test it, and set up CI/CD basics for a production-grade delivery.
Feature Engineering & Modeling (Tabular Data)
3 daysGo deeper on tabular ML: feature design, leakage-proof validation, calibration, and model interpretation for decision making.
Data Quality for ML (Validation, Drift & Monitoring Basics)
2 daysMake ML reliable by improving data quality: validation rules, training/serving parity, drift signals, and monitoring foundations.
NLP with Transformers (Practical Fine-tuning)
3 daysFine-tune transformer models for classification and extraction tasks with strong evaluation and deployment patterns.
MLflow: Experiment Tracking & Model Registry
2 daysTrack experiments, compare runs, manage models with registry, and standardize the ML lifecycle for teams.
Time Series Forecasting (From Baselines to Delivery)
3 daysForecast demand and KPIs with proper backtesting, strong baselines, feature-based models, and delivery-ready outputs.
Data Engineering for ML (ETL, Quality, Features)
3 daysBuild reliable datasets for ML: validation, feature-ready tables, and training/serving parity foundations.
Need a custom version?
We can tailor duration, format, and industry use-cases for your teams.
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