MLOps knowledge has become one of the major skills that one machine learning engineer can have. However, putting a machine learning model into production successfully is not an easy task. It requires a wide range of software development and DevOps abilities in addition to data science understanding. In a nutshell, in order to increase your value as a machine learning engineer, you must not only understand how to apply various Machine Learning and Deep Learning models to a specific problem, but also how to test, verify, and deploy them. Having someone who can put Machine Learning models into production has become a major benefit for any business. One of the final problems, when it comes to putting Machine Learning models into production, is verifying that API that is serving this model is having good performance.