Machine learning (ML) is a branch of AI that enables computers to learn and make human-like predictions. ML infrastructure refers to a combination of solutions that aid the entire ML development lifecycle. These solutions mainly come as software platforms that assist in building, training, and monitoring ML models as well as those that provide synthetic data and data annotation services for training. It also includes a handful of hardware solutions providers that develop graphics processing units (GPUs) and application-specific integrated circuits (ASICs) specifically designed for ML applications.
ML is no longer a niche offering limited to a select few companies. Today, it has gained widespread adoption across various industries, with emerging use cases such as cybersecurity and digital twins. As companies strive to gain a competitive edge, they are increasingly relying on ML to extract insights from big data and drive informed decisions. This trend is set to boost the growth of ML infrastructure players, who will benefit directly from the increased adoption of ML. Additionally, the surge in ML adoption is expected to catalyze innovation, leading to scalable and more efficient solutions.
Machine learning (ML) has moved beyond being a novelty limited to large enterprises. Its adoption is expanding across various industries, as companies use the tech to increase process efficiency and derive insights from significant data volumes.
Integrated MLOps players, with their end-to-end ML Infrastructure, have supported organizations across various industries to help train, deploy, and monitor ML models. Their services find widespread applications in areas like business intelligence, demand/sales forecasting, and application development, among others. Moreover, synthetic data providers are playing a crucial role in generating the necessary training data for ML models, while ML hardware vendors are empowering the training process for these applications.
We have identified such use cases below:
ML infrastructure comprises 1) software solutions that are required to develop, train, deploy, and monitor AI/ML models; 2) providers of training data such as synthetic data; and 3) the hardware needed to train and run ML workloads.
Companies in the Integrated Machine Learning Operations (MLOps) segment offer end-to-end solutions that handle the development, training, deployment, and monitoring of models using a single platform. This was the highest-funded segment and had the most incumbent activity.
Incumbents across this space include Big Tech companies such as Microsoft, Amazon, and Google, which offer MLOps and related solutions that complement their existing cloud services. It also includes chipmakers like NVIDIA that specialize in hardware such as graphics processing units (GPUs) and other compute solutions for AI/ML applications.
Incumbents operating in the ML Infrastructure Software space include large cloud vendors such as Microsoft, Amazon, and Google. These players are mainly present in the MLOps segment, providing solutions that complement their cloud computing stack (such as Azure and AWS), giving enterprises that already use their services an easy point of entry into ML infrastructure solutions. Their solutions are largely developed in-house, with partnerships and acquisitions to expand product capabilities.
In the Hardware space, NVIDIA is one of the most dominant players, with its graphics processing units (GPUs) and proprietary compute solutions. Other chipmakers such as Intel and AMD are catching up, as they start integrating AI capabilities into their products. For both these companies, acquisitions are also a key part of growth strategy.