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, IBM Watson, and Domino and open-source packages like TensorFlow and Anaconda. 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, ElectrifAi, 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. And a final class of ML/AI vendors occupy niche use cases and specializations, such as Anyscale’s excellence in scalability, Labelbox’s specialty for labeling multimedia data for ML model training, or Character.ai’s inventive capabilities for creating personas for conversational AI and chatbots. Across these many ML/AI vendors, we see varying levels of spending intention and utilization across enterprises, with the more robust MLOps offerings occupying the leading positions and niche applications trailing.
Positioning for the ETR Observatory on ML/AI was determined by ETR’s two core, syndicated surveys. Full methodology and graphic explanation are available on our About the ETR Observatory page.
This ETR Observatory report examines the vendors within a subsector grouping by triangulating data from ETR’s Technology Spending Intentions Survey (TSIS), Emerging Technology Survey (ETS), commentary from ETR Insights interviews with IT decision makers (ITDM) from the ETR Community, and proprietary industry analysis by our research staff.
As ML/AI matures and more enterprises invest in large-scale use cases for the technology, the focus for ML/AI has shifted toward quality, scale, management, and speed. Early proofs of concept become enterprise-wide ML/AI deployments baked into day-to-day operations only with investment in better ML workflows (MLOps), faster computing, and bigger and more reliable data sets. Vendors in the ML/AI market seem to be focused either on delivering excellence in a niche, such as ease of creating model training data; delivering convenience with easy off-the-shelf applications and pre-trained models that lead to quick business gains; or providing full, end-to-end MLOps platforms. ETR’s TSIS data show all sectors declining in spend year-over-year, but ML/AI remains relatively strong, with the second-highest sector Net Score at 42%, behind only the Container Orchestration sector at 45%.
The roots of ML/AI remain firmly in an open-source ethos, and many data scientists prefer open-source tools and free programming languages to do their work. The enterprise ML/AI market is a complicated one, then, with nearly every tool providing integration and support for languages such as Python and platforms such as Jupyter Notebook at minimum. Some of the vendors tracked in ETR’s ML/AI sector are indeed open-source tools themselves that have expanded to offer paid, premium-level services like managed infrastructure alongside their popular free products.
Compared to the fates of other sectors, the ML/AI sector does not appear to be quite as affected by the gravity of the big public cloud platforms and organizational pushes to consolidate onto a single vendor. In the video conferencing and productivity applications sectors, for instance, it is increasingly common to see organizations moving to align their choices to the public cloud platform in which they have made large-scale investment, such as an organization switching to Teams and Project when they commit to a broad Microsoft license. In the ML/AI sector, however, data science teams still seem to have a lot of latitude and independence to experiment and to choose the environment that works best for their data science needs. An organization that is largely a Microsoft shop, for example, may have robust ML/AI activity in another platform, such as Google or AWS. Time will tell whether consolidation pressures will extend to the ML/AI realm, but as data science programs mature and establish tighter integrations with an enterprise’s security, data and analytics, and infrastructure array, we will likely see more concerted effort to consolidate ML/AI and align to the organization’s dominant public cloud platform.
Figure 1. Microsoft is the stand-out leader in the ML/AI sector in both Net Score and Pervasion. The other two major public cloud players, AWS and Google, follow behind in Pervasion, with AWS holding the second-highest Net Score. Databricks is third in Net Score and fourth in Pervasion, and a cluster of other vendors hover behind. SparkCognition, OpenText Magellan, and Oracle have just single-digit Net Scores, and IBM Watson is the only vendor in the sector with a negative Net Score.
I. Generative AI’s Moment
In late 2022, OpenAI made headlines when it made ChatGPT widely available, an interface where users could interact with a kind of chatbot to produce sophisticated AI-generated responses. Though in development for several years prior, the arrival of ChatGPT caught the attention of the press, business, and popular culture in a way no other previous generative AI or large language model (LLM) tool had. ChatGPT has been called a “Sputnik moment” in how it has launched a new AI race, and pundits have been quick to polarize into the two typical camps we see anytime a new technology breaks onto the scene: those seeing the technology as a revolutionizing force for utopian transformation, and those seeing it as a serious threat to the established order and calling for regulation. In a recent ETR Insights interview, the IT manager for a midsize municipal government remarked that this enthusiasm will ultimately be good for AI in general: “One of the good things about OpenAI, ChatGPT, and things like that is the increase of public awareness. People that I wouldn’t expect to be asking me about AI are now asking about AI […] which is great because it gives everyone a chance to have those types of discussions and potential use cases.” Just a few months in, and we already see many companies weaving ChatGPT and similar products into their enterprise tech offerings, such as Microsoft with its $10 billion investment in OpenAI and its rollout already of ChatGPT in its Microsoft 365 products under the Copilot branding.
As with any new technology, the hype around generative AI and LLMs will give way to more evenhanded discourse and practical achievements at scale tempered by rational controls. Until then, however, the hype for generative AI in general and for OpenAI in particular remains high. ETR added OpenAI to the February 2023 ETS, and right away it posted the highest evaluation rate in the entire survey. It remained atop the heap in the May 2023 survey, with a whopping 46% evaluation rate, which tracks plans to evaluate and current evaluation indications. Despite the high evaluation rates captured in the ETS, most organizations have still not shifted to utilization of generative AI tools, suggesting that clear business use cases are not yet evident for many businesses. In the April 2023 Macro Views survey, ETR asked respondents for what business use cases they were considering generative AI and LLM tools. More than half indicated they were not even evaluating these tools yet, but among those who were considering generative AI, customer support, text and data summarization, and code generation and documentation were the most common uses.
II. ML/AI at Enterprise Scale with Robust MLOps Capabilities
As an organization shifts from experimentation with its first data science projects to full-scale deployment of ML/AI, the focus becomes ML model management and reuse, monitoring, governance, and ultimately automation. Many of the vendors that provide these capabilities – collectively called MLOps, which draws from DevOps principles of continuous delivery – are leaders in ETR’s spending intentions data, demonstrating deepening investment in complete and scalable platforms. The big three cloud platforms offer these complete capabilities in the form of Microsoft’s Azure Machine Learning, Google’s Vertex AI, and Amazon SageMaker, and these platforms are striving toward greater ease of use and accessibility. The VP of Business Intelligence and Analytics for a midsize financial services enterprise remarked in a recent ETR Insights interview that he liked Azure ML because it was “self-service” and did not require “a DevOps competency” to navigate. He said with “Azure, I was able to get my data scientists, plug them in, and just let them start working with the team, and we were able to go from training to deployment in a couple of weeks.” Microsoft, AWS, and Google have the highest Pervasion in the ML/AI sector, according to TSIS data. Microsoft’s Net Score looms large in the sector at 67%, followed by AWS at 55% and Google at 47%. Large legacy vendors like Oracle and IBM also offer full MLOps capabilities through Oracle Machine Learning and IBM Watson Machine Learning, respectively. Both have low Net Scores in recent TSIS cycles and Pervasion under 20%. Oracle has slowly gained ground in Pervasion in recent surveys and held relatively steady with single-digit Net Score, while IBM Watson has dropped considerably in Net Score into negative territory and is showing contracting Pervasion. Some smaller vendors, such as Domino, focus on enabling robust MLOps and collaboration without organizations having to worry about managing infrastructure. Domino has seen Net Sentiment increase year-over-year in the ETS, but it remains low in absolute terms at just 8% as of the May 2023 survey.
Open-source platforms Anaconda and TensorFlow offer support for ML model deployment and end-to-end management in both free licenses and in paid tiers with added support and storage. Anaconda provides a way to manage versions of Python and packages and deploy projects, while TensorFlow offers a more complete MLOps tool set but is known for its specialty in training for deep learning use cases. In a recent ETR Insights interview, the VP and Chief Data Architect for a large consumer goods company noted his organization uses platforms like Google and Databricks “for a certain class of problems,” but will turn to “more generic tools like Python and TensorFlow to build more complicated regression models.” In another interview, the CIO and CISO for a nonprofit medical research institute added that TensorFlow is “a solid name” with a “great community ecosystem,” an important attribute for the success of any open-source project. May 2023 ETS data shows TensorFlow and Anaconda with the third and fourth highest Net Sentiment in the ML/AI sector, trailing stand-out OpenAI as well as Databricks. TensorFlow’s Net Sentiment declined a bit from May 2022 levels, down four percentage points to 34%, while Anaconda’s Net Sentiment has remained steady at 28% for multiple surveys.
III. Democratizing Data Science to Speed Time to Business Value
A CIO shares his thoughts about names in the ML/AI sector. Listen to which vendors he calls "legacy names" and what his says about "next-movers".
This Insights Interview features an interview with the VP of Business Intelligence and Analytics who has centralized all data under Snowflake and leverages Azure for Infrastructure-as-a-Service and AI/Ml. Watch to hear his feedback on tools such as Azure, Databricks, Dataiku, and ChatGPT.
IV. Tools with Unique Value
A cluster of other ML/AI vendors can be categorized based on the kind of unique value or specialized niche use case they offer to the broader ML/AI landscape. Anyscale, which emerged from a university lab, focuses on scalability, helping organizations make the leap from development to enterprise-level, sustainable production on ML/AI. Despite the name, it is not to be confused with Scale AI, a vendor focused on providing high quality training data for training ML models and AI applications. Further into a niche use case is Labelbox, a collaborative platform for labeling training data in a variety of formats, including text, images, and video. Lastly, Character.ai allows users to develop their own chatbot characters and personas and hold full conversations with these different characters powered by generative AI. Still in its early stages, the tool could have a number of uses from customer service chatbots to digital assistants to therapy and counseling.