Machine Learning Data Lifecycle in Production

Go to Course: https://www.coursera.org/learn/machine-learning-data-lifecycle-in-production

Introduction

### Course Review: Machine Learning Data Lifecycle in Production With the rise of machine learning (ML) as a vital component of modern technology, the demand for proficient data engineers and ML practitioners who can adeptly manage the data lifecycle in production environments has surged. Coursera's **Machine Learning Data Lifecycle in Production** course promises to equip learners with the essential skills to handle this complex yet crucial aspect of machine learning. This review will delve into the course's strengths, educational value, and overall recommendation for prospective learners. #### Course Overview As the second installment of the **Machine Learning Engineering for Production Specialization**, this course focuses on the practical aspects of data management, including gathering, cleaning, validating datasets, and ensuring data quality. You'll engage deeply with TensorFlow Extended (TFX), a robust framework designed to facilitate ML model deployment processes. The culmination of knowledge in this course will allow learners to establish a comprehensive data lifecycle while utilizing data lineage and provenance metadata tools. #### What You Will Learn **Week 1: Collecting, Labeling, and Validating Data** The course kicks off with a crucial introduction to the operational aspects of machine learning. You'll gain insights into TFX, allowing you to adeptly collect, label, and validate data, thus preparing it for production use. This week sets a solid foundation, emphasizing the importance of clean, well-organized data in successful machine learning applications. **Week 2: Feature Engineering, Transformation, and Selection** In the second week, the focus shifts to feature engineering—a key skill for optimizing the performance of ML models. Participants will learn how to apply various techniques for data transformation and feature selection, particularly for both structured and unstructured data types. Addressing class imbalances will also be covered, which is crucial for achieving equitable model performance. **Week 3: Data Journey and Data Storage** Here, learners will explore the entire journey of data within a production system. This includes understanding how to leverage ML metadata and enterprise schemas to accommodate fast-evolving data. Knowing how to navigate this landscape is critical for maintaining the efficiency and accuracy of ML models over time. **Week 4 (Optional): Advanced Labeling, Augmentation, and Data Preprocessing** In this optional week, you’ll explore advanced themes such as combining labeled and unlabeled data to enhance model accuracy. The techniques discussed for data augmentation will help diversify your training set, which is fundamental to building robust machine learning models. #### Why Choose This Course 1. **Hands-On Project Work**: This course is designed with a practical approach, allowing you to work through real-world scenarios and challenges. Engaging with TFX offers direct experience that bridges theory and practice. 2. **Comprehensive Syllabus**: The syllabus strikes a balance between foundational knowledge and advanced techniques. It caters to a range of learners, from those new to data lifecycle management in ML to seasoned practitioners looking to sharpen their skills. 3. **Expert Instruction**: Coursera collaborates with leading universities and organizations, ensuring that the instruction is of high quality and relevance to industry standards. 4. **Flexibility and Accessibility**: With the ability to learn at your own pace, this course provides flexibility for working professionals and students alike. #### Recommendation In conclusion, the **Machine Learning Data Lifecycle in Production** course on Coursera is highly recommended for anyone looking to deepen their understanding of data management within machine learning systems. With comprehensive content, practical applications, and a structured learning path, this course is ideal for both aspiring data engineers and experienced ML practitioners seeking to refine their skills. Whether you are looking to enhance your career or simply gain valuable knowledge in this fast-evolving field, this course is a worthwhile investment into your professional development.

Syllabus

Week 1: Collecting, Labeling and Validating Data

This week covers a quick introduction to machine learning production systems. More concretely you will learn about leveraging the TensorFlow Extended (TFX) library to collect, label and validate data to make it production ready.

Implement feature engineering, transformation, and selection with TensorFlow Extended by encoding structured and unstructured data types and addressing class imbalances

Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data.

Combine labeled and unlabeled data to improve ML model accuracy and augment data to diversify your training set.