The Sequence Scope: The MLOps Space is Getting Crowded and Confusing

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📝 Editorial: The MLOps Space is Getting Crowded and Confusing

MLOps is one of the most popular and overloaded terms in modern machine learning. Typically associated with platforms that manage different aspects of ML models, MLOps seems to be used indiscriminately today to describe everything from model training to monitoring. As a result, it becomes really confusing for organizations and data science teams trying to assemble MLOps capabilities in their ML pipelines. Just this week, I read separate press releases about funding rounds for MLOps startups like Comet and Snorkel, which operate in areas as different as model monitoring and data labeling respectively. So don’t feel bad if you are confused about MLOps😉

🗓 Next week in TheSequence Edge:

Edge#79: What is Few-Shot Learning; Prototypical Networks as One of the Most Popular Few-Shot Learning Architectures; TorchMeta is the OpenAI Gym of Meta-Learning

🔎 ML Research

Generating Text from Data

🤖 Cool AI Tech Releases

Lookout for Equipment

💸 Money in AI

  • MLOps startup Snorkel AI raised a $35 million Series B round and introduced Application Studio, a visual builder with templated solutions for common AI use cases and easy construction of new and custom use cases (currently in preview). Incubated at Stanford University in 2016, Snorkel became a very popular open-source project for data labeling. Snorkel Flow is an end-to-end platform built on the principles of the Snorkel project.
  • MLOps startup Comet raised $13 million in a Series A funding round. (We covered them in Edge#11). Comet is one of the machine learning platforms that’s gaining increasing traction within data science teams. The platform streamlines the creation of ML models and experiments across different frameworks.
  • Streamlit raised $35 million in Series B funding. It’s an open-source app framework for ML and Data Science teams, helping them turn data scripts into data apps. All in Python.
  • No-code data lake engineering platform Upsolver raised $25 million. They simplify transforming raw data into queryable data through a visual SQL UI, and automate hundreds of data lake engineering tasks to optimize performance.
  • ML monitoring platform Aporia raised a $5 million seed round. They claim that the core of their platform is a strong ML monitoring engine topped by a flexible, collaborative UX that “turns monitor configuration and modification into an effortless — even fun — experience”.
  • Synthetic data startup Synthesis AI raised $4.5 million in its funding round. Using a proprietary combination of generative neural network and cinematic CGI pipelines, it creates a vast amount of synthetic perfectly-labeled data to build more capable computer vision models.
  • Computer vision development platform CrowdAI raised a $6.25 million Series A financing round. It’s an end-to-end, no-code platform that builds custom AI to automate visual inspection for clients and help them analyze imagery and video.

CEO of IntoTheBlock, Chief Scientist at Invector Labs, I write The Sequence Newsletter, Guest lecturer at Columbia University, Angel Investor, Author, Speaker.

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