In the wake of the pandemic, enterprises across the world have doubled down on artificial intelligence (AI) and machine learning to accelerate their digital journeys. The digitisation demand has called for new processes to split, train, test, develop, and deploy machine learning models. MLOps, or machine learning operations, is born out of this need.
Developments like Microsoft’s Nuance acquisition (for $19.7 billion) and the growing interest of venture capital firms in the sector point to the high-growth potential of MLOps startups.
For instance, in April 2020, New York-based MLOps startup Hive raised $85 million in funding at a valuation of $2 billion. MLOps startup Comet raised $13 million in a Series A funding round led by Scale Venture Partners. The list goes on.
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From the beginning of 2021 through April 12, MLOps space saw close to 442 investment deals worth $11.65 billion. In 2020, AI and ML startups witnessed 1,601 funding rounds worth $27.49 billion, as reported by TechCrunch.
According to Deloitte, MLOps platforms could generate annual revenue of $4 billion by 2025. AI, if implemented successfully, could generate about 20% of a company’s earnings. the report added.
In India, however, the MLOps is still a fledgling sector. Below, we have listed MLOps startups developing systematic tools and processes for building and deploying machine learning models.
AIVID TechVsion
Based in Ahmedabad, AVID TechVsion was founded by Dhaval Vora and Nikhil Jain in 2020. The company has developed an inspection automation platform that empowers enterprises to mitigate the risk of non-compliance and strove towards a safe, efficient and high-quality workplace by automating human observation-based process inspection using artificial intelligence (AI) and computer vision
AIVID platform helps teams automate visual inspection tasks and manage standard operating procedures (SOP) by analysing camera feeds. Also, it allows them to identify process compliance risks in real-time, discover business insights and drive efficiency across locations. The company claimed the technology has applications across retail, hospitality and smart-infrastructure industries.
DataOrc
DataOrc was founded by Mayur Jadhav and Navdeep Agarwal in 2018. The company enables businesses to make data-driven decisions. DataOrcs builds practical machine learning solutions with data, right from POCs to deploying machine learning models.
Evok Analytics
Evok Analytics was founded by Rohan Havaldar and Vishal Pathania in 2015. The company offers business analytics, data engineering, data science and decision-support solutions, leveraging machine learning algorithms and Big Data. Evok works with different datasets, including syndicated and POS data, to come up with cutting-edge solutions.
OpsLyft
Aayush Kumar founded San-Francisco and Noida-based MLOps startup OpsLyft in 2019. The company leverages artificial intelligence and analytics to deliver insights and automate routine workflows that help software or machine learning teams build, test, and deploy any workload on the cloud with no downtimes and at the lowest possible infrastructure spend.
OpsLyf claims to ease cloud management. In July 2019, the company had raised pre-seed funding from undisclosed investors. It counts Inshorts, Innovaccer, Blackbuck, and Meesho as its clients.
Scribble Data
Scribble Data was founded by Indrayudh Ghoshal and Venkata Pingali in 2016. It offers a machine learning feature store for data teams of mid-market enterprises. Scribble Data also helps businesses develop and manage production-ready datasets required to scale their machine learning and analytical use cases and applications.
Tredence
Shub Bhowmick, Shashank Dubey and Sumit Mehra co-founded San Jose and Bengaluru-based data science and AI engineering company Tredence in 2013. The company provides actionable and quantifiable analytics solutions to marketing, sales and operational teams. Its industrialised machine learning operations platform ‘ML Works’ helps enterprises manage several AI customer engagements. It allows them to scale machine learning models, reduce outages in the machine learning pipeline, and simplify model monitoring.
Tredence helps organisations ensure their models in production are relevant, contextual and provides deeper visibility to data scientists for faster value realisation. With ML Works, Tredence aims to make ML adoption simple, pragmatic and accessible.
As of December last year, Tredence has raised close to $30 million in funding from Chicago Pacific Founders, a strategic healthcare investment fund.
