AutoML Training is a practical two-day program designed to help professionals understand how automated machine learning simplifies model development, selection, and optimization. This course focuses on the principles, workflows, and real-world use cases of AutoML, enabling participants to accelerate machine learning projects, reduce manual effort, and make informed decisions when adopting AutoML tools while maintaining responsible and trustworthy AI practices.

• Understand AutoML fundamentals
• Learn automated machine learning concepts
• Understand AI workflow automation basics
• Gain knowledge of model selection techniques
• Learn data preprocessing workflows
• Understand model training and optimization
• Explore AI automation applications
• Identify practical AutoML use cases

• Data analysts and business analysts
• Machine learning beginners and practitioners
• AI and analytics professionals
• Product managers working with ML solutions
• Technology professionals exploring low-code AI platforms

• Basic understanding of machine learning or data analytics concepts
• Familiarity with datasets and predictive problem statements
• Awareness of Python or ML workflows is beneficial
• No advanced programming or data science expertise required

• Understanding AutoML concepts and workflows
• Identifying problems suitable for AutoML
• Interpreting AutoML-generated models and results
• Evaluating benefits and limitations of AutoML
• Applying responsible and ethical AutoML practices

By the end of this training, participants will be able to explain how AutoML works, identify suitable use cases, interpret and evaluate automatically generated models, understand limitations and risks, and apply AutoML responsibly to accelerate machine learning initiatives.

Introduction to AutoML
• What AutoML is and why it is used
• AutoML vs traditional machine learning workflows
• Components of an AutoML pipeline
• Common AutoML use cases

AutoML Model Selection and Optimization
• Automated algorithm selection concepts
• Hyperparameter tuning and search strategies
• Feature engineering and preprocessing automation
• Understanding trade-offs between speed and accuracy

Interpreting AutoML Outputs
• Understanding generated models and metrics
• Model comparison and selection
• Confidence, uncertainty, and validation
• Avoiding blind reliance on automated results

AutoML Across Business and Industry Use Cases
• AutoML for classification and regression problems
• Forecasting and anomaly detection use cases
• Business value and ROI considerations
• When AutoML is not the right choice

Responsible and Governed AutoML
• Bias and data quality risks in AutoML
• Explainability and transparency challenges
• Governance and auditability of automated models
• Responsible adoption guidelines

Integrating AutoML into ML Workflows
• AutoML within broader ML and MLOps pipelines
• Collaboration between business and technical teams
• Scaling AutoML across organizations
• Building an AutoML adoption roadmap

• AutoML fundamentals
• Automated machine learning workflows
• Data preprocessing techniques
• Model selection concepts
• Model training and evaluation
• Hyperparameter optimization basics
• AI workflow automation
• Predictive analytics concepts
• Performance optimization techniques
• Practical AutoML scenarios

• AutoML use case identification exercise
• Model interpretation and selection discussion
• Responsible AutoML scenario analysis
• Final knowledge evaluation quiz

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in AutoML Training, validating their expertise in understanding AutoML concepts, workflows, use cases, limitations, and responsible adoption.