TensorFlow Vs Pycaret: A comparison of machine learning frameworks

As companies are deploying more and more machine learning models into their systems, a variety of frameworks (some open-source, some not) have come up over the years to make this deployment faster and more efficient. Some of the popular frameworks include TensorFlow, Amazon SageMaker, IBM Watson Studio, Google Cloud AutoML, and Azure Machine Learning Studio, among others. Tensorflow, by far, takes one of the top spots when it comes to machine learning frameworks that technologists depend on. Recently, Pycaret, a low-code machine learning library in Python, has also become increasingly popular among ML practitioners.  Let us take a look at how both of them work and what makes them different from each other. TensorFlow Recently completing six years, TensorFlow was developed by the Google brain team at first for internal use. Then, its initial version came out under the Apache License 2.0. Open source in nature, TensorFlow has an entire ecosystem of tools and libraries that