Last year, the machine learning industry crossed a billion dollars in market value with many benefits across various industries. Experts project its future value to reach $8 billion by 2025. A huge part of this growth can be ascribed to machine learning (ML) frameworks that allow for the deployment of accurate models and applications. This article delves into the ML framework and its key features.
What Is a Machine Learning Framework?
Machine learning frameworks have been growing in adoption over recent years, and many people may have seen popular ML frameworks like Amazon Machine Learning, Apache Mahout, H2O, and TensorFlow. But what do these frameworks do, and how do they work?
A machine learning framework is an interface, library, or tool that enables developers to build ML models more easily. These technologies largely rely on algorithms, and machine learning democratizes ML algorithms, making them accessible for all, including those with little tech backgrounds. This means anyone can develop an ML model without understanding or creating the underlying algorithms. Machine learning frameworks afford developers a wide collection of pre-built and optimized templates providing end-to-end support for the machine learning process.
Many machine learning frameworks exist today for several purposes, and each may take a lot of time and effort to learn and master.
Generally, Python is the predominant machine learning programming language. However, you may consider several other factors when picking your framework, including your budget and goal.
Main Features of the ML Framework
Before choosing your framework, it’s important to know that machine learning frameworks come with several features, which are detailed below.
Usability
Machine learning users come from different backgrounds, including data science and statistics. They demand ease-of-use features and tools to use ML frameworks and benefit from their ML projects. Therefore, business users may consider tools like interactive notebook environment, project management and collaboration, and traceability. What’s more, some of the best tool options on the market come in handy for users with zero code backgrounds. Some platforms even provide end-to-end visual workflow and collaboration features to enhance transparency and usability.
Cost
Providers price their services differently based on the market’s unique needs. Some cloud providers offer pay-as-you-go pricing models and have a marketplace for users to purchase specific algorithms based on their pressing needs. Other providers offer subscription models, which may not be cost-effective, especially for small and medium-sized businesses.
Extensibility, Adaptability, and Scalability
Businesses deal with changing environments all the time. With deep learning algorithms’ predictive accuracy benefits, businesses can remain agile through multiple case scenarios and advance. A typical ML framework can provide features to aid with their extension and scalability plans. Therefore, choosing your framework based on such features can be a good idea. Also, understanding how ML frameworks adapt to the existing technology stack is very important, and users must ensure their chosen platforms are cloud-agnostic before making the final decision.
Model Development
Models are essential in machine learning, and their development is one of the key factors in predicting any future problem. Generally, ML frameworks facilitate model development in two ways: auto ML and traditional development. Auto ML automates tasks, as the name suggests. It supports automating tasks like feature engineering, hyperparameter tuning, and selecting the best model. Traditional model development, on the other hand, provides features for users to manually develop models from scratch.
All in all, machine learning frameworks have significantly impacted businesses across industries. In choosing your machine learning frameworks, it’s advisable to decide based on your unique challenges and user needs. Use the guide above to get started and determine the best framework for your business.
