The following content and technical points will be introduced in this article: As the commercial values of machine/deep learning have risen, the software technologies for ML have also changed each day. Concepts, such as training, model, algorithm, predictions, inference, together with software frameworks, such as Spark MLlib and Tensorflow, are frequently referenced. The Jupyter notebook can be used on a local machine to call Tensorflow to train tens of thousands of images. After continuous parameter adjustments, the output model inference/prediction results are accurate. There is a figure in the paper published in the NIPS entitled, Hidden Technical Debt in Machine Learning Systems, which shows one thing very accurately. In the process of generating commercial value, the workloads of MLOps are much larger than the core development of machine learning for the development of peripheral settings for machine learning. MLOps in the production environment vary depending on the business scenario. Additionally, it involves many modules, which cannot be explained in one article. This article will focus on one of the modules, the machine learning pipeline. This article will introduce how to use Alibaba Cloud Serverless services to improve the efficiency of R&D and O&M and automatically convert algorithms to trained models. By doing so, it is expected to finally generate business value after testing and approval through prediction/inference in the production environment.