The AI Technology Ecosystem: A Market Taxonomy

Artificial intelligence(AI) and machine learning(ML) are growing in popularity in the technology ecosystem. Just a few months ago, it was relatively simple to keep up with the developments in the AI and ML markets. Today, that seems like a daunting task for most technologists as the space have been evolving incredibly fast.

The explosion of AI and ML platforms have created a very crowded market in which is very hard to distinguish signal from noise. However, despite the large number of AI technologies and startups, we can identify a few main categories that provide a good taxonomy to better understand the AI-ML markets. Let’s take a look:

Cognitive API Platforms

Cognitive API platforms abstract capabilities such as image, text and vision analysis using pre-trained AI models exposed as APIs.

— Some Platforms in the Space: Watson Developer Cloud, Microsoft Cognitive Services, Google Cloud, NLP, Speech APIs

— Good For: Performing well-defined tasks in areas such as text analytics, image processing and audio analytics.

— Not Great For: Implementing custom AI algorithms, training models…

Cloud ML Platforms

Cloud ML platforms are cloud services that abstract the creation and execution of ML applications.

— Some Platforms in the Space: Azure ML, Google Cloud ML, AWS ML.

— Good For: Creating cloud-first, large scale ML applications

— Not Great For: Integration with on-premise data sources, custom algorithms…

On-Premise ML Platforms

On-premise ML platforms enable the implementation of ML applications both on cloud and on-premise infrastructures.

— Some Platforms in the Space: Spark ML, Flink ML,

— Good For: Implementing custom algorithms, extensible and customizable ML applications.

— Not Great For: Simple ML scenarios, multi-language ML solutions…

Self-Service Data Science

Self service data science platforms provide interactive mechanisms for AI-ML applications.

— Some Platforms in the Space: Apache Jupyter, Apache Zeppelin, Google Cloud Datalab

— Good For: Integrative ML application, data exploration-visualization, human-centric ML solutions…

Deep Learning Frameworks

Deep learning frameworks are developer frameworks that provide the fundamental constructs to build deep learning applications.

— Some Platforms in the Space: TensorFlow, Torch, Caffe.

— Creating custom algorithm, customizable deep learning applications…

— Not Great For: Basic deep learning models [ex: image recognition, etc]…

Self-service ML Platforms

Self-service M Platforms allow non-developers to implement simple ML applications.

— Some Platforms in the Space: BigML, GE

— Good For: Customizing well-defined ML models.

— Not Great For: Implementing complex ML solutions…

Vertical AI Solutions

Vertical AI solutions focus on the implementation of industry-specific, complex AI solutions powered by some of the aforementioned technologies.

— Some Platforms in the Space: IBM Watson, DeepMind.

— Good For: Implementing sophisticated, domain-specific AI solutions.

— Not Great For: Mainstream AI-ML models…

AI-ML Marketplaces

As a group, AI-ML marketplace technologies enable the cataloguing, management and monetization of AI-ML assets such as algorithms, data sets, etc.

— Some Platforms in the Space: Algorithmia

— Good For: Discovering and using custom AI algorithms

— Not Great For: Implementing end-to-end AI-ML solutions.

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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