This course focuses on the importance of building machine learning models that are built to last and can be effectively deployed in production. While much attention is given to model training and parameter tuning, the reality is that a significant percentage of experimental models never make it to production. By shifting your mindset from a machine learning engineering perspective to an MLOps (Machine Learning Operations) mindset, you will gain the skills necessary to train, document, maintain, and scale your models to their fullest potential. One key aspect covered in this course is the ability to experiment and document with ease. While experimenting with ML models can be enjoyable, it can also be time-consuming. This course will teach you how to design reproducible experiments that expedite the process, while also emphasizing the importance of writing documentation for yourself and your teammates. By doing so, future work on the pipeline will become much easier. Another important topic covered in this course is building MLOps models for production. You will learn best practices for packaging and serializing both models and environments to ensure their longevity in a production environment. This will enable your models to last as long as possible and be effectively utilized in real-world scenarios. Additionally, this course will teach you how to scale up and automate your ML pipelines. By considering the complexity of both the model and the data, as well as implementing continuous automation, you can ensure that your models are ready for production use. Furthermore, you will learn how to effectively monitor and deploy your models in a timely manner. Upon completion of this course, you will possess the skills necessary to design and develop machine learning models that are ready for production. Furthermore, you will have the ability to continuously improve these models over time, ensuring their long-term success.