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 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. Pycaret 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. Features TensorFlow TensorFlow works with different programming languages like Python, C++, Javascript, Java, etc. It also comes with the option of running on multiple CPUs and GPUs and is available on macOS, Windows, Android, iOS, and 64-bit Linux. Pycaret 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. Working areas TensorFlow 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.js – It is a machine learning library to develop models in JavaScript and can use machine learning in the browser or in Node.js directly. A developer can build and train models using flexible APIs directly in JavaScript. 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 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 deploymentHigh and powerful performanceScalability – takes projects from research to productionEfficient library management Advantages of Pycaret As a low-code library, Pycaret, too, has its own set of advantages. Some of them are: Increased productivityEasy deploymentBusiness 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.
As both are popular choices when it comes to ML model deployment, let’s look at how they work and what makes them different from each other
As both are popular choices when it comes to ML model deployment, let's look at how they work and what makes them different from each other
["AI (Artificial Intelligence)", "Deep Learning", "Machine Learning", "models", "performance", "PyCaret", "Tensorflow"]
["performance", "scikit-learn", "Amazon SageMaker", "machine learning", "spaCy", "AI", "models", "ML", "Machine Learning", "PyCaret", "XGBoost", "LightGBM", "Deep Learning", "TensorFlow", "Azure", "AI (Artificial Intelligence)", "Tensorflow"]
["AI", "machine learning", "ML", "Amazon SageMaker", "TensorFlow", "scikit-learn", "XGBoost", "LightGBM", "spaCy", "Azure"]
I am a technology journalist at AIM. What gets me excited is deep-diving into new-age technologies and analysing how they impact us for the greater good.

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