The manufacturing industry is one of the largest industries in the world, vitally supporting the economies of many countries across the globe. With the growing deployability of artificial intelligence (AI), manufacturers are turning to AI to turn their production plants into more efficient smart factories. Smart factories have contributed towards improving worker safety and their high efficiency means that they can deliver quality products faster to their customers. As the manufacturing industry embraces machine learning, demand for user-friendly tools that can deploy complex machine learning models with relative ease for engineering professionals has been growing over the years. In particular, deep learning tools need a considerable amount of programming knowledge and, thus, remain obscure to engineers inexperienced with programming. To overcome these barriers, we propose ManufacturingNet, an open-source machine learning tool for engineers which will enable them to develop and deploy complex machine learning models by answering a few simple questions. We also have curated ten publicly-available datasets and benchmarked the performance using ManufacturingNet‘s machine learning models. We obtained state-of-the-art results for each dataset and have included pre-trained models with our package. We believe ManufacturingNet will enable engineers around the world to deploy machine learning models with ease. The GitHub repository for ManufacturingNet can be found at https://github.com/BaratiLab/ManufacturingNet. Keywords: Manufacturing, Deep Learning, Programming, ManufacturingNet