As companies increasingly deploy machine learning models in their systems, a variety of frameworks (some open source, some not) have emerged 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 is by far one of the top places 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’s take a look at how the two work and what makes them different from each other.
After six years, TensorFlow was first developed by the Google Brain team for internal use. Then, its initial version was released under the Apache 2.0 license. Open source by nature, TensorFlow has a comprehensive ecosystem of tools and libraries that help developers build and deploy applications based on machine learning. TensorFlow 2.7.0 was also recently released and offered improved debugging experience, public convolution, automatic sharing of data services, and more.
Also open source by nature, PyCaret is a low-code machine learning library in Python. It helps data scientists to perform end-to-end experiments efficiently. This allows them to go from preparing data to deploying their model in minutes.
Pycaret is gaining popularity over other ML libraries as it provides a low-code alternative library that can perform complex machine learning tasks with just a few lines of code. It is built around several libraries and machine learning frameworks such as scikit-learn, XGBoost, Microsoft LightGBM, and spaCy, among others.
With its various updates and releases over the years, TensorFlow has found applications in various areas of deployment. Some of them are:
- Tensor Processing Unit (TPU) – This is an application-specific AI accelerator (ASIC) integrated circuit that works for neural machine learning using TensorFlow. A few years ago, Google announced that TPUs would be available in beta on Google Cloud Platform.
- Tensorflow 2.0 – TensorFlow released version TensorFlow 2.0 in September 2019 with some major upgrades. It comes with more intuitive APIs with better resource documentation. TensorFlow also modularized the platform based on semantic version control with this release.
Pycaret works in various areas of the machine learning platform. Some areas include:
Model performance analysis
Since analyzing the performance of a trained ML model is crucial, Pycaret comes with 60 plots that can assess and explain model performance and deliver results instantly without writing complex code.
Preparing data in PyCaret
It operates in different segments of data preparation with a high degree of automation.
- Data preparation – Imputation of missing values, unique hot-coding, ordinal coding, cardinal coding, normalization, transformation
- Feature engineering – Feature interaction, polynomial features, trigonometry features, group features, bin number features, rare level combination
- Feature Selection – Feature Importance, Remove Multicollinearity, Principal Component Analysis, Ignore Low Variance
Benefits of TensorFlow
There are many factors that make TensorFlow famous. Some of them are:
- Easy deployment
- High performance and powerful
- Scalability – takes projects from research to production
- Efficient library management
Benefits of Pycaret
As a low-code library, Pycaret also has its own set of benefits. Some of them are:
- Increased productivity
- Easy deployment
- Business ready solution
Although TensorFlow is still leading the way in popularity, Pycaret may compete with it in the future due to its easy to deploy nature. As machine learning is a large and complex field, while choosing which framework option to use, one needs to choose the framework that will maximize their performance and work efficiently for their systems.