As a data engineer, the following are the steps and tools that can be used for deploying Machine Learning (ML) models:
Model selection and training: The first step is to select an appropriate ML algorithm and train it on the relevant dataset. This step can be performed using tools such as Scikit-learn, Keras, TensorFlow, or PyTorch.
Data preparation: Once the model is trained, the next step is to prepare the data for deployment. This may involve cleaning the data, transforming it into a suitable format, and normalizing it. Tools like Pandas, NumPy, and Scikit-learn can be used for this purpose.
Model export: The trained model needs to be exported to a format that can be easily used for deployment. This may involve exporting the model to a binary file format such as HDF5 or to a serialized format such as JSON or YAML. The choice of format depends on the specific requirements of the deployment environment.