What Are Machine Learning Frameworks?

Written by Coursera Staff • Updated on

Machine learning frameworks make working with complex deep learning and machine learning models accessible. Discover details about popular machine learning frameworks, how they work, and how you can start building your career with them.

[Featured Image] Two coworkers stand together in an office and talk about their favorite machine learning frameworks while looking at a smartphone and tablet.

Scrolling through your social media feed, asking a chatbot a question, or translating a piece of text are all examples of machine learning in action. Machine learning is a type of artificial intelligence that means a computer has the ability to learn on its own without needing human intervention or explicit programming. Machine learning uses statistics and algorithms to recognize patterns and create answers or solutions accordingly. These algorithms are incredibly complex, making them challenging to work with unless you have a background in data science.

Machine learning (ML) frameworks offer various interfaces, tools, and libraries that take these complex algorithms and make them simpler to work with, even without a deep understanding of the technology. Read on to learn more about machine learning frameworks, including some popular Python machine learning frameworks, and how to build a career that involves working with these frameworks.

What are machine learning frameworks?

Machine learning frameworks are tools, libraries, and resources that provide a standardized way for developers and other professionals to tailor the machine learning algorithm’s functionality without comprising the underlying code. The frameworks provide a simpler user interface that is accessible to many professionals instead of making it necessary to know and read code. Most of these are Python machine learning frameworks, primarily because Python is the most popular machine learning programming language [1].

Notable popular machine learning frameworks include:

  • Scikit-learn: A Python library that supports both unsupervised and supervised learning. If you’re new to machine learning, Scikit-learn is a great choice. It’s effective for predictive data analysis and feature engineering.

  • PyTorch: A customizable option that uses building classes. If you’re a Python developer searching for a framework with a shorter learning curve, Pytorch, when stacked against other frameworks, might be the one for you. Additionally, it’s an open-source, deep-learning framework.

  • TensorFlow: A popular end-to-end machine learning platform that offers feature engineering and model serving. Finally, if you need a framework with robust scalability that works across a wide range of data sets, TensorFlow is a good choice.

What are machine learning frameworks used for?

Machine learning frameworks use many machine learning and deep learning functions, including time series, natural language processing, and computer vision. They can primarily help users deal with complex, challenging algorithms by making them more accessible and easy to use.

Finally, in contributing to the machine learning lifecycle, machine learning frameworks expedite processes such as data visualization, model development, testing and logging, and data engineering.

How do machine learning frameworks work?

Machine learning frameworks overlay a user interface with complex machine learning algorithms. For many businesses—maybe even yours—the time, expense, and infrastructure requirements of using machine learning make it impractical for your organization to build it. Machine learning frameworks remove much of this expense by providing a ready-to-use resource that includes tools, libraries, and interfaces, thereby removing up-front expenses and time requirements.

If you’re a business owner, you can choose a machine learning framework that fits your company’s needs and provides the tools for your developers to interact with the machine and deep learning processes without needing to understand the complex algorithms being employed. This way, your developers have the opportunity to extract the value from the machine learning algorithms without risking compromising them by interacting with the code directly.

Who uses machine learning frameworks?

Many industry sectors, such as finance, cybersecurity, health care, insurance, and retail, employ machine learning frameworks. Using these ML frameworks can provide your business with the important data produced by machine learning models, supporting informed decision-making. Developers working within these industries access the different open-source machine learning frameworks to create tailored machine learning processes to meet their employers’ goals and needs.

Pros and cons of using machine learning frameworks

Two pros of machine learning frameworks include simplifying ML algorithms and scalability, while two cons can be selecting the right framework for your needs and the source(s) where your ML algorithm is obtaining its data.

More specifically, one benefit of using a machine learning framework includes simplifying machine learning algorithms, which makes the technology much more accessible to businesses across different industries without needing to employ professionals trained in deep learning programming languages. This allows your business to discover insights and make data-driven decisions that would otherwise be inaccessible. To uncover these insights, machine learning frameworks can process vast amounts of data, also known as scalability, to identify patterns and reach conclusions.

One challenge of machine learning frameworks is that you risk spending money and time purchasing one that doesn’t meet your needs. It’s important to consider what exactly you’re hoping to get out of machine learning and then take the time to research the different types of frameworks to ensure you’re choosing the best one for your business.

Knowing where your machine learning algorithms are sourcing their information is also important. Not all data is usable or accurate, and you’ll need to ensure that the sources are transparent, unbiased, and valuable. Otherwise, you risk extrapolating results that aren’t actually true, which might lead your business astray with inaccurate conclusions.

How to get started in machine learning frameworks

If you’re interested in pursuing a career that involves machine learning, the first thing you’ll want to do is learn more about machine learning and artificial intelligence. Many careers within the field require a background in computer technology or data engineering, so it’s important to expose yourself to these concepts and ensure you enjoy them before applying for the education necessary to make yourself competitive in the field.

One option is to look for online platforms that provide the opportunity to build machine learning processes and algorithms so that you have the chance to practice and develop these skills. Additionally, online message boards, social media groups, and even professional conferences are all viable options for increasing your knowledge in machine learning so that your resume has the necessary skills for consideration in this type of role.

For example, one career that involves machine learning frameworks is that of a machine learning engineer. In this profession, you could be responsible for designing the machine learning software and frameworks that others, such as data scientists, use to run different models. To build a career as a machine learning engineer, you will likely need at least a bachelor’s degree in a subject such as computer science. Fortunately, once you become a machine learning engineer, you could potentially earn an average annual base salary of $126,013, according to Glassdoor’s January 2024 data [2].

Getting started with Coursera

Sharpen your machine learning skills and learn about machine learning frameworks with courses and degrees offered on Coursera. With choices such as Stanford and DeepLearning.AI’s Machine Learning Specialization, you’ll learn about the foundational skills and knowledge needed to pursue a career in machine learning and artificial intelligence.

Article sources


DeepLearning.AI. “Five Important AI Programming Languages, https://www.deeplearning.ai/blog/five-important-ai-programming-languages/.” Accessed January 19, 2024.

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