Machine Learning Frameworks to work in 2021

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How do you choose a machine learning framework?

When choosing a machine learning framework, it’s important to understand what type of data you have and what type of applications you want to build. “The reason why people talk about deep learning and unstructured data is that it is the only form of machine learning that can do that today,” said Gualtieri.

Here we have Top Frameworks to work:

  • TensorFlow
  • PyTorch
  • Scikitlearn
  • Spark-ML
  • Torch
  • Huggingface
  • Keras

Let’s hash it out.


TensorFlow is a free and open-source software library for Machine Learning developed by Google Brain. Tensorflow computations are expressed as stateful dataflow graphs. The name TensorFlow derives from the operations that such neural networks perform on referred to as tensors.

The movement of this data on the system is known as flow, hence the named TensorFlow.

  • Perform regression, classification, neural networks, etc.,
  • TF is a symbolic math library based on data flow and differentiable programming.
  • TF is one of the fast, flexible, and scalable open-source ML libraries.


Pytorch is an open-source Machine Learning library developed by (FAIR) Facebook’s AI Research Lab based on the Torch library used for applications such as Computer Vision and NLP.

Provides 2 high-level features:
  • Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU)
  • Deep neural networks built on a type-based automatic diffrentiation system
Leading Competitor to TensorFlow:
  • It does Regression, classification, neural networks, etc.
  • Runs on both CPUs and GPUs


Scikit-learn is an open library for data analysis written in Python for general purposes.

It features

1) Classification
2) Regression
3) Clustering Algorithms includes

  • Support Vector Machine
  • Random forests
  • Gradient boosting
  • K-Means

and is designed to interoperate with Python numerical and scientific libraries NumPy and Scipy.

Spark ML

Spark-ML is a new package introduced ib 1.2, which aims to provide a uniform set of high-level APIs that help users create and tune a practical machine learning pipeline.

Spark-ML standardizes APIs for ML algorithms to make it easier to combine multiple algorithms into a single pipeline, or workflow.

Creating a linear regression model with Spark-ML to feed the data to it, after which will be able to make predictions.

Spark ML is complicated, but instead of having to work with NumPy arrays, it lets you work with Spark RDD data structures, which anyone using Spark in its big data role will understand. And you can use Spark ML to work with Spark SQL data frames, which most Python programmers know. So it creates dense and sparks feature-label vectors for you, taking away some complexity of preparing data to feed into the ML algorithms.


Torch claims to be the easiest ML framework. It is an old ML library first released in 2002. It is an open-source ML library, a scientific computing framework, and a scripting language based on the Lua programming language.

  • It provides a wide range of algorithms for deep learning.
  • It provides a flexible N-dimensional array or Tensor, which supports basic routines for indexing, slicing, transposing, type-casting, resizing, sharing storage, and cloning.

Torch has been extended for use on Andriod and ios. It has been used to build hardware implementation for data flows like those found in neural networks.

New Machine Learning Framework types:


Maker of the popular PyTorch-Transformersmodel library, Hugging face, today said it’s bringing its NLP library to the TensorFlow machine learning framework.

Hugging is a popular open-source, which creates good base models for researches built on top of TensorFlow and PyTorch . They adapt complicated tools, such as GPT-2, to work easily on your machine.


There are many deep learning frameworks available today. Why use Keras..?

Here are some of the areas in which Keras compares favorably to existing alternatives.

  • Keras priorities developer experience.
  • Keras has broad adoption in the industry and the research community.
  • Keras makes it easy to turn models into products.
  • Keras has strong multi-GPU and distributed training support.
  • Keras is the nexus of a large ecosystem.
  • It is a python-based framework that makes it easy to debug and explore.
  • Its primary usage is classification or summarization, other language functionalities like translation, speech recognition, and tagging are also a part of its usage.

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