Machine learning has become almost ubiquitous in our lives. Training these models have even entered mainstream with the advent of efficient deep learning frameworks and advancements in hardware innovations. There are, however, two noteworthy limitations that prevent practitioners outside the machine learning community from successfully training models that achieve the desirable performance. The first one is the heavy requirement of expert knowledge. There are many components around building a successful machine learning model that each one could impact its final performance. Selecting these components is not easy: it requires trials and errors and a lot of human knowledge! The second is the inefficiency of training deep learning models, especially in the problem picking a good pretraiend feature extractor for a given task. This work tackles both of these problems by incorporating AutoML techniques. As introduced and detailed in later chapters, these frameworks could effectively automate many processes and complexity within training machine learning models.