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Enterprises’ urgent need is for startups to help solve getting more machine learning (ML) models into production. That’s because 87% of data science projects never make it into production. Algorithmia’s 2021 enterprise trends in machine learning of 750 business decision-makers found 22% say it takes between one and three months to deploy an ML model into production before it can deliver business value. Furthermore, 18% say it takes over three months to get a model into production. Delays getting ML models into production are symptoms of larger, more complex problems, including lack of production-ready data, integrated development environments, and more consistent model management. According to IDC, 28% of all AI and machine learning projects fail because of these factors. Closing the gaps in MLOps and across the entire model lifecycle process creates a lucrative new market opportunity for startups, valued at $4 billion by 2025. According to Dr. Ori Cohen’s research, there’s been $3.8 billion in funding already.
The state of MLOps shows startups in the lead
Cohen, lead researcher at New Relic, recently published an exhaustive analysis of the MLOps landscape, The State of MLOps. He hosts the analysis on AirTable for ease of viewing and querying the data set he’s created. Selecting the Category option under the Views menu shows the five categories of companies included in his analysis. Cohen’s analysis is shown below, with companies sorted by category.
The following are insights from the State of MLOps analysis:
- 88% of the State of MLOps are startups, dominating every category in the analysis and leading funding. ML Platform startups lead all categories on funding with $3.4 billion. Databricks, DataRobot, and Algorithmia have together raised $2.9 billion alone. Data Monitoring is the second-most funded area of MLOps, with $116.3 million raised to date. ML Monitoring is the third-most funded MLOps category with $105 million. The average funding level by MLOps startup is $110 million, based on the State of MLOps analysis.
- Data Ops/Data Engineering is the dominant persona MLOps companies concentrate on today. Half of all MLOps companies are concentrating on Data Ops/Data Engineering as their primary persona. 14 of the 17 companies concentrating on this persona are startups. Amazon SageMaker and Google Vertex AI are the largest MLOps products to attract and sell their solutions to this persona. $3.5 billion in funding is driving new solutions for this persona, 93% of all funding in MLOps. Data Scientist/ML Engineer is the second-most targeted persona, with 13 companies focusing on these roles’ needs. Microsoft Azure and IBM OpenScale concentrate on the Data Scientist/ML Engineer persona in their solution development and messaging.
- Most MLOps startups are concentrating on Tabular Data first and then expanding into other data types to differentiate. The State of MLOps shows a common progression MLOps startups make from mastering Tabular Data with their unique Data Governance, Data Monitoring, ML Monitoring, ML Platforms, and Serving Platforms first, then expanding into other data types. In addition, startups most often add in Data Quality, Data Integrity, and Pipeline Integrity to further differentiate themselves from the many startups who start with Tabular Data as their main data focus.
- MLOps is a market ripe for Private Equity investors looking for M&A opportunities and investors looking to get into AI. Cohen predicts vendor consolidation in the MLOps space, with the largest competitors buying mid-size companies. He predicts that mid-size MLOps companies will begin buying the smallest ones to become more valuable to the largest companies. His analysis of the state of MLOps shows three acquisitions already. The gaps enterprises face moving models into production require a scale level that favors mid-tier and larger startups. Look for Private Equity investors to fund mid-tier MLOps leaders into aggregator roles, acquiring multiple MLOps startups at once to create valuable acquisition for larger vendors who need the Intellectual Property (IP) and patents smaller, faster-innovating startups can provide.
The goal of MLOps is to manage and accelerate the lifecycle for analytics and ML models from development into production. Enterprises aren’t getting the yield rates or scale from ML models they’re spending months creating because they are too many data quality, data integrity, data model management, and a series of other challenges that block their progress. Startups bring much-needed insight, innovation, and urgency to solving these problems, receiving $3.4 billion in funding to date. Vendor consolidation in MLOps is inevitable as larger, slower-moving companies look to startups for the innovative spark and insight they need to energize their platforms and deliver the scale and solutions their enterprise customers need to get more value from their ML models.
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