Learning AutoML is your practical guide to applying automated machine learning in real-world environments. Whether you're a data scientist, ML engineer, or AI researcher, this book helps you move beyond experimentation to build and deploy high-performing models with less manual tuning and more automation. Using AutoGluon as a primary toolkit, you'll learn how to build, evaluate, and deploy AutoML models that reduce complexity and accelerate innovation. Author Kerem Tomak shares insights on how to integrate models into end-to-end deployment workflows using popular tools like Kubeflow, MLflow, and Airflow, while exploring cross-platform approaches with Vertex AI, SageMaker Autopilot, Azure AutoML, Auto-sklearn, and H2O.ai. Real-world case studies highlight applications across finance, healthcare, and retail, while chapters on ethics, governance, and agentic AI help future-proof your knowledge. Build AutoML pipelines for tabular, text, image, and time series data Deploy models with fast, scalable workflows using MLOps best practices Compare and navigate today's leading AutoML platforms Interpret model results and make informed decisions with explainability tools Explore how AutoML leads into next-gen agentic AI systems

Hands-on session using AutoML to make advanced predictions.

Hands-on session using AutoML to make advanced predictions.

Join dotData Sr. Data Scientist Sharada Narayanan as she dives into the strengths, uses, and limitations of popular time-series forecasting techniques like ARIMA and Prophet. Sharda will walk through real-world examples, share code snippets, and explore how ARIMA Prophet compare when building models using Feature Engineering techniques and advanced machine learning algorithms. Dive into the insights of each method and see how programmatic feature engineering and machine learning can supercharge your time series analysis.