Databricks for Data Scientists: ML on the Lakehouse

Module 1: Data Scientist Workspace Orientation

• Databricks vs local Jupyter: collaboration, scalability, and compute advantages
• Cluster configuration for data science: GPU selection and library management
• Notebook best practices: parameterised notebooks and reproducible experiments
• Accessing data: reading from Delta tables, Unity Catalog, and S3 buckets

Module 2: Exploratory Data Analysis at Scale

• Pandas on Spark (Koalas): running familiar Pandas code on distributed data
• Visualisation in notebooks: matplotlib, seaborn, and Plotly for EDA
• Profiling large datasets: using data profile widget on Indian census microdata
• Correlation analysis: identifying signals in e-commerce click-stream data

Module 3: Feature Engineering & Feature Store

• Designing features for Indian consumer behaviour prediction models
• Feature Store workflow: creating, writing, and reading features consistently
• Point-in-time lookups: preventing data leakage in time-series training sets
• Feature sharing across teams: reducing duplication in a data science platform

Module 4: Scalable Model Training

• Training sklearn pipelines on Databricks: from single-node to distributed
• Spark ML Pipelines: fitting models natively on distributed DataFrames
• Hyperopt and SparkTrials: parallel hyperparameter search across worker nodes
• AutoML for rapid prototyping: baseline models for Kolkata traffic prediction

Module 5: MLflow for Data Scientists

• Experiment tracking: automatically logging every model run with autolog()
• Comparing experiments: identifying the winning model across 100+ trials
• Model flavours: logging scikit-learn, XGBoost, PyTorch, and custom models
• Reproducibility: re-running any experiment from a saved MLflow run ID

Module 6: Deep Learning & NLP on Databricks

• PyTorch and TensorFlow on Databricks: single-node and Horovod distributed training
• Hugging Face integration: fine-tuning BERT for Indian language sentiment analysis
• Petastorm: efficient data loading from Delta Lake for deep learning workloads
• GPU cluster strategies: managing cost for iterative deep learning experimentation

Module 7: Model Deployment for Data Scientists

• From notebook to production: packaging models with MLflow pyfunc wrapper
• Batch scoring: running inference on new customer data using Spark DataFrames
• Model Serving: deploying real-time prediction APIs without DevOps dependency
• Model monitoring: detecting drift in a Hindi NLP classifier over time

Module 8: Capstone & Best Practices

• Capstone: end-to-end ML project -- Mumbai property price prediction on Lakehouse
• Code quality for data scientists: modular code, testing, and version control
• Collaboration patterns: working with data engineers on shared pipelines
• Career guide: Databricks certifications relevant for data scientist roles in India

Experience in the Industry Learn from Databricks-certified data engineers and ML engineers who have built production lakehouse platforms, Delta Live Tables pipelines, and ML workflows on Databricks for data-driven enterprises.

Backgrounds at the Top Our Databricks trainers have delivered data platform projects at technology companies, financial services firms, and healthcare organisations where Databricks is the central platform for data engineering and machine learning.

Clear & Effective Teaching Databricks architecture, Spark DataFrames, Delta Lake, Databricks SQL, MLflow, Delta Live Tables, Unity Catalog, and governance are explained clearly with real lakehouse data platform examples.

Hands-On Learning Focus Students build end-to-end lakehouse pipelines, work with Delta tables, create DLT pipelines, track ML experiments with MLflow, and configure Unity Catalog governance through structured lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Databricks platform releases, Databricks AI and GenAI capabilities, Unity Catalog enhancements, and evolving lakehouse architecture best practices.