ICYMI: the ETR Observatory report and Market Array data set for Machine Learning and Artificial Intelligence are now available. Backed by proprietary survey data, vendors are positioned in Leading, Advancing, Tracking, or Pursuing vectors according to Momentum and Presence in the market. The plotting of the vendors in the subsector is based on direct customer utilization and evaluation, not opinions or vendor influence.
The Observatory Scope
The plotting of vendors across the Observatory Scope is supported wholly by ETR’s exclusive market intelligence and spending intentions data sets (see Figure above). The Leading vector in this period is populated by OpenAI (ChatGPT), Microsoft Azure Machine Learning, Amazon SageMaker, Meta Llama, Google (Vertex AI), Databricks, Hugging Face, and TensorFlow. ChatGPT leads in overall Presence in the survey, while Microsoft Azure Machine Learning leads in Momentum. Anthropic (Claude) is the sole vendor in the Advancing vector, showing relatively high Momentum but lower Presence than the Leading vector.
IBM Watson, Oracle, and Anaconda sit in the Tracking vector, with high Presence but relatively lower Momentum. Finally, DataRobot, Cohere, H2Oai, Jasper, Dataiku, and C3ai occupy the Pursuing vector, with Presence and Momentum that are relatively lower than those of their sector peers. However, the tight range near the center of the vector axis demonstrates the favorable positioning of most players included in this report. This report will break down the four vectors and the vendors in more detail in the following sections.
Introduction
The ML/AI sector today tracks vendors targeting organizations in the full spectrum of maturity with regard to data science programs. Organizations with more mature programs largely built data science teams that embraced a variety of tools, most certainly open-source programming languages like R and Python and open-source tools like Jupyter Notebook and TensorFlow. But as programs evolve, reproducibility and reuse of ML models become important, as well as all of the governance and data integration activities and monitoring that are part of end-to-end MLOps frameworks. Organizations that have more recently begun their data science journeys may leap-frog these phases given a variety of turnkey, business user-friendly solutions from today’s vendors focused on “democratizing” data science. No matter the stage of maturity, however, there is no denying the impact of generative AI’s recent emergence in the zeitgeist. It seems most organizations are now at least exploring possible use cases for ML/AI, and vendors in a variety of sectors – from security to RPA to enterprise applications – are weaving AI capabilities into their core product offerings.
The ML/AI market reflects this diversity of maturity and use cases, with classes of vendors that speak to the needs of different organizations. Large public cloud platforms like Microsoft, AWS, and Google offer full MLOps capabilities for enterprise-scale data science programs, competing alongside other popular end-to-end offerings like Oracle and IBM, and open-source packages like TensorFlow and Anaconda. Generative AI-focused offerings like Meta Llama, Anthropic’s Claude, and OpenAI’s ChatGPT offer large language models (LLMs) to help accelerate organizations’ generative AI use cases. Another class of tools aims to simplify the complexity of data science work by offering business-user-friendly products like pre-trained ML models to speed time to business value, such as DataRobot, C3.ai, H2O.ai, and Hugging Face. Still, others have broadened their appeal as ML/AI platforms by focusing earlier in the data and analytics pipeline, such as Databricks with its popular data lakehouse paradigm for data management or Dataiku with its finesse as a data preparation tool. Acknowledging Snowflake’s growing presence in this space, ETR intends to begin tracking the company in our ML/AI sector beginning with the next TSIS period. Snowflake’s positioning in this market will be included in future reports. Across these many ML/AI vendors, we see varying levels of spending and utilization across enterprises, with the more robust MLOps offerings and the generative AI-focused products occupying leading positions.
Continue to the full report on ETR's public-facing website here
Request the underlying data source, The ETR Market Array for ML/AI Tools, by reaching out to our research team at insights@etr.ai
Enterprise Technology Research (ETR) is a technology market research firm that leverages proprietary data from our targeted IT decision maker (ITDM) community to provide actionable insights about spending intentions and industry trends. Since 2010, we have worked diligently at achieving one goal: eliminating the need for opinions in enterprise research, which are often formed from incomplete, biased, and statistically insignificant data. Our community of ITDMs represents $1+ trillion in annual IT spend and is positioned to provide best-in-class customer/evaluator perspectives. ETR’s proprietary data and insights from this community empower institutional investors, technology companies, and ITDMs to navigate the complex enterprise technology landscape amid an expanding marketplace. Discover what ETR can do for you at www.etr.ai