In this talk, we’ll explore the complete machine learning model building process, providing data scientists and ML engineers with practical insights and strategies for success. We will examine each phase of the model life cycle, from data ingestion and pre-processing to feature engineering, model selection, training and fine-tuning. We’ll explain best practices for model evaluation, validation and deployment, including effective MLOps integration to ensure seamless model monitoring and scalability. We will highlight real-world case studies and common pitfalls encountered during model development, offering actionable solutions and strategies to overcome challenges. We’ll emphasize optimizing workflows to improve performance and ensure reproducibility in complex projects. Join us to gain a deeper understanding of the entire model building process and gain insights that will help you build robust, efficient and scalable models to drive impactful business outcomes and support continuous innovation.