Machine Learning Market Analysis

Machine learning continues to gather momentum among large enterprises and startups alike. In 2020, the total ML market value was estimated at $22.59 billion, and this figure is projected to increase up to $126 billion in 2025 with the compound annual growth rate of 42.08%. Risk management, trading, automation and performance analysis remain the top ML applications businesses use to boost their results, drive sales and increase market share. With more than $28 billion of total funding allocated to AI, this technology has the potential to substitute a great deal of manual work and drive greater automation. Thus, companies can significantly increase performance and gain the competitive edge. What do all these figures mean for your business? It is already clear that machine learning is not a flop as some experts projected. ML has become an inseparable part of industries and brought such meaningful advancements as telemedicine, self-driving cars, hyper-targeted ads and tons of other useful solutions that already benefit entrepreneurs, making their businesses more profitable and customer-centric. What is more, machine learning is insanely accessible and affordable today. You don’t have to invest lots of funds in research or building an innovative solution. Instead, you validate your idea with PoC within 3-7 weeks and build an MVP in 6 months. With a reliable vendor, chances are you’ll get the first version of your product quickly and at an affordable price!

How Big Companies Contribute to the Development of ML

While you’re only searching for the ways to apply ML in business, startups and big behemoths have made their names by leveraging the power of AI to create smart solutions that disrupted their industries at some point of their development. The good news is that most of these solutions are accessible for a broad audience. You can pay an affordable fee and use them to build your own models that could be further integrated into your product. This approach reduces the cost of ML development by several times and enables anyone from any industry to leverage the power of AI. Thus, you can develop a simple ML model starting at $4k! But keep in mind that the final cost of development is highly dependent on your needs, requirements and type of implementation. Also, it is worth mentioning that there is an ever-growing community of AI enthusiasts, who constantly contribute with their software, products and other developments to the AI marketplace. Thus, if you need an open-source ML solution of your product, chances are you’ll find it here. Below we listed some of the services released by large corporations that are frequently used to build and train ML models: IBM IBM Watson Machine Learning is the product that enables developers and data scientists to launch their data science and ML projects smoothly. Its scalable and open-source model operation software provides companies with an opportunity to harness the power of AI for their purposes. Google TensorFlow is an open-source machine learning platform that helps software engineers build and train ML models. It equips practitioners with a flexible and comprehensive ecosystem of libraries, tools, and other community resources that significantly contribute to AI development. Microsoft This IT giant could not but benefit from the growing potential of ML and launched a series of AI-based products. Azure Machine Learning is one of the services catered by Microsoft that helps companies to build and deploy ML software faster. Apart from Azure, Microsoft has put much effort into developing the AI direction. The company has recently launched a deepfake tool. Video Authenticator provides the user with the “confidence score” that the picture, video or any other media has been manipulated, helping the audience to differentiate between authentic and synthetic content. Intel Intel is one of the leading adopters of machine learning on the global market. The company applies ML software to decide on its pricing strategy, optimize expenses and apply large-scale data analysis. It also implemented machine learning to analyze IoT data streams. The company also offers a set of tools and platforms to accelerate ML deployment. For instance, Intel’s Deep Learning SDK is widely used for ML model training and deployment, while AI Analytics Toolkit is applied to accelerate machine learning and data science pipelines.

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