Machine Learning Application Development: Business Insights [With Infographic]

Machine learning app development

Predicting the weather, recommending new songs and movies, diagnosing diseases — these are just a few of the many machine learning applications that your business can offer the world.

If you’re considering developing a machine learning application and want to learn more about the current state of the market, possible use cases, and how to actually do it, you’ve come to the right place.

Our infographics will give you all the information you need in the most convenient and concise way. So let’s get started!

Machine learning market in focus

The machine learning market has seen explosive growth in recent years, with its global market expected to reach $209.91 billion in size by 2029. This growth is driven by the increasing demand for advanced analytics and the need for automation and intelligent decision-making.

Here are some more statistics.

Machine learning market stats

Why build a machine learning application?

Here are the facts that explain why businesses opt for implementing machine learning and creating ML-powered applications.

Reasons to build a machine learning app

Learn how businesses can apply machine learning in 10 different ways

Technology stack for machine learning app development

The machine learning application development process requires at least 6 categories of tools: frameworks and libraries, APIs and SDKs, data preprocessing and analysis, visualization tools, cloud platforms, and DevOps tools.

Machine learning app development tech stack

5 steps to build a machine learning application

Check out what the process of building an ML-driven application looks like.

Machine learning application development steps

Find out more about three essential ML models that your business will use to build an application

What machine learning apps can you build for your industry?

Here are some examples of machine learning applications that can be developed for different industries.

Industry-specific examples of machine learning applications

Final thoughts

Machine learning applications are not limited to the areas mentioned above. If you have an idea for how to use this innovative technology in your industry, it might well also be possible and profitable.

Are you ready to make the transition to an ML-powered business? Turn to our machine learning development team and we will help you shape your ML-driven growth strategy, consult with you on how to create a machine learning application, and build one for you.

Don’t wait to innovate — start your project now!


Anastasiya Haritonova

Technical Writer

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