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.
Mastering the End-to-End Machine Learning Model Building Process: Best Practices and Pitfalls
Session Speakers
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Paril Ghori
Senior Data Scientist, Caterpillar
Paril Ghori is a results-driven data scientist with a proven track record in machine learning, deep learning, NLP and MLOps, specializing in financial analytics, anomaly detection and predictive modeling. With expertise in Apache Spark, PyTorch, TensorFlow and cloud computing, Paril has built anomaly detection models that saved thousands of manual hours and identified financial anomalies worth millions of dollars. He has also spearheaded the development of AI-powered sentiment analysis for earnings call transcripts, helping businesses understand market impact on stock prices. Beyond modeling, Paril has optimized MLOps pipelines, automated cloud-based analytics and developed scalable anomaly detection frameworks. His work on NLP-driven customer insights and marketing mix models has increased ROI and operational efficiency. Passionate about innovation, Paril thrives at the intersection of AI, cloud computing and business intelligence, continuously pushing the boundaries of data science to create real-world impact.
