Enroll Course: https://www.coursera.org/learn/machine-learning-data-lifecycle-in-production
The ‘Machine Learning Data Lifecycle in Production’ course on Coursera is an essential resource for anyone looking to deepen their understanding of data management in machine learning systems. As part of the Machine Learning Engineering for Production Specialization, this course offers a comprehensive look into building robust data pipelines that are crucial for real-world ML applications. The curriculum covers practical skills such as gathering, cleaning, and validating datasets to ensure quality data input. You will learn how to leverage TensorFlow Extended (TFX) for feature engineering, transformation, and selection, maximizing the predictive power of your data. Moreover, the course delves into managing the data lifecycle by utilizing data lineage and provenance tools, enabling you to track data evolution throughout your systems. The weekly modules are well-structured, starting with data collection and labeling, moving through feature engineering, and concluding with data storage and management strategies. An optional advanced section explores data augmentation and preprocessing techniques to further enhance model performance. Whether you’re an aspiring data engineer or a seasoned ML practitioner, this course provides practical skills and insights necessary to deploy reliable, scalable machine learning systems. I highly recommend it for professionals aiming to elevate their data infrastructure and ensure the longevity of their ML models in production environments.
Enroll Course: https://www.coursera.org/learn/machine-learning-data-lifecycle-in-production