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.
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 helps developers build and deploy machine learning backed applications. TensorFlow 2.7.0 was also released recently and featured improved debugging experience, public convolution, data service auto-sharding, etc.
Also open source in nature, PyCaret is a low-code machine learning library in Python. It helps data scientists perform end-to-end experiments efficiently. It allows them to move from preparing data to deploying their model within minutes.
Pycaret is rising in popularity in comparison to other ML libraries, as it provides an alternate low-code library that can perform complex machine learning tasks with only a few lines of code. It is built around several machine learning libraries and frameworks such as scikit-learn, XGBoost, Microsoft LightGBM, and spaCy, among others.
Through its various updates and versions over the years, TensorFlow has found applications in various areas of deployment. Some of them are:
- Tensor Processing Unit (TPU) – It is an AI accelerator application-specific integrated circuit (ASIC) that works for neural machine learning using TensorFlow. A few years back, Google announced that TPUs would be available in beta on the Google Cloud Platform.
- Tensorflow 2.0 – TensorFlow released the TensorFlow 2.0 version in September 2019 with some major upgrades. It comes with more intuitive APIs with better documentation of resources. TensorFlow also modularised the platform based on semantic versioning with this release.
Pycaret works in a variety of areas in the machine learning platform. Some areas include:
Model performance analysis
As analysing the performance of a trained ML model is crucial, Pycaret comes with 60 plots that can evaluate and explain model performance and give results instantaneously without writing any complex code.
Data Preparation in PyCaret
It works in different segments of data preparation with a high degree of automation.
- Data preparation – Missing Values Imputation, One Hot Encoding, Ordinal Encoding, Cardinal Encoding, Normalisation, Transformation
- Feature engineering – Feature Interaction, Polynomial Features, Trigonometry Features, Group Features, Bin Numeric Features, Combine Rare Levels
- Feature selection – Feature Importance, Remove Multicollinearity, Principal Component Analysis, Ignore Low Variance
Advantages of TensorFlow
Many factors contribute to the fame of TensorFlow. Some of them are:
- Easy deployment
- High and powerful performance
- Scalability – takes projects from research to production
- Efficient library management
Advantages of Pycaret
As a low-code library, Pycaret, too, has its own set of advantages. Some of them are:
- Increased productivity
- Easy deployment
- Business ready solution
Though TensorFlow still takes the lead in terms of popularity, Pycaret can give stiff competition to it in the future due to its easy-to-deploy nature. As machine learning is a vast and complex area, while choosing the option of which framework to use, one must choose the framework that will maximise their performance and work effectively for their systems.