It is not an easy task to design and build systems that involve Machine Learning and Data Science requirements. In addition to this, managing the complexity of intelligent systems requires careful planning and execution. We will talk about how to use different tools and services to perform machine learning experiments ranging from fully abstracted to fully customized solutions. We will show how these are done with different ML libraries and frameworks such as Scikit-learn, PyTorch, TensorfFlow, Keras, MXNet, and more. In addition to these, I will also share some of the risks and common mistakes Machine Learning Engineers must avoid to help bridge the gap between reality and expectations. While discussing these concepts, tools, frameworks, and techniques, we will provide several examples and recipes on how these ML workflows and systems solve different business requirements (e.g., finance, digital transformation, automation, sales).