The following is a very preliminary sketch of the landscape of AI technologies (i.e. frameworks, libraries, platforms and product offerings). Please feel free to send enhancement suggestions via the contact form. Thank you in advance.

The Linux Foundation’s AI and Data Landscape is an interesting resource as well.

Deep Learning




Neural Network Modeler

Also see visualization solutions like Facebook ActiVis.


Traditional ML & Data Science

Machine Learning Frameworks

Reinforcement Learning

Please see the dedicated overview article. Still debating with myself whether I should add all the references to a map.

Natural Language Processing

NLP Architectures



Speech Synthesis

Automatic Speech Recognition (ASR)

NLU & Conversational AI

  • Rasa – RL-driven NLU for dialog systems and virtual assistants

Other NLP

Computer Vision


Embodied Agents

Recommender Systems


Symbolic Approaches



Optimizers & Solvers

These are only a few solvers – more extensive lists can be found on NEOS and YALMIP.



Constraint Programming

Satisfiability (SAT)

Theorem Provers & Proof Assistants

Many more can be found in “A Survey on Theorem Provers in Formal Methods” which provides a great overview.

Logic Programming

Knowledge Bases & Ontologies

Also see Linked Open Data Cloud.

Knowledge Graph Technologies

AI Service Orchestration

Model and Data Catalogs

Model Zoos & Catalogs

Primarily Commercial Model Marketplaces

Data Catalogs

Data Formats

Microsoft MMdnn can be used to convert models between frameworks. [There are additional related projects like onnxconverter. Apple has Core ML Tools, but it only converts to CoreML format.]

Cloud AI Services

This still needs to be extended and reorganized quite heavily. Relevant Cloud AI Service Portfolios to look at include:

Automatic Speech Recognition (ASR)

Natural Language Processing, Understanding and Conversational AI

Speech Synthesis




Workflow & Service Orchestration

Data Ingestion, Lakes & Warehouses

Systems & Platforms

AI Platforms

There are many more platforms and also several specialized niche ones like Facebook ELF for (strategy) game research or AllenAct for Embodied AI. It is sometimes difficult to classify whether something is primarily a platform or framework.

Cloud AI

AI Development Experience & IDEs

Model Training and Lifecycle Management

Acceleration & Distribution

Hyperparameter Optimization (HPO)

Automated AI (AutoAI) and Neural Architecture Search (NAS)

The University of Freiburg maintains the great page about the underlying techniques at

Notebooks & Dashboards

There are many related product offerings like Google Colab, binder, ContainDS. PixieDust is a tool to encapsulate business logic from notebooks.


Neuromorphic Hardware

See WikiChip for more neural processors. And AI-Chip.



While deciding what to measure is a topic on its own and e.g. discussed in SUPERB: Speech Processing Universal PERformance Benchmark, in Chollet’s excellent On the Measure of Intelligence as well as in Adams’ I-athlon: Towards A Multidimensional Turing Test, here are a few software projects that help with it:

Probabilistic Programming


Oftentimes AI relies on cloud technology for its training and serving infrastructure. Hence, it makes sense to also list a few cloud technologies. This section is under construction and will be extended soon. Until then the CNCF Cloud Landscape is a good reference.


Service Meshes

Service Mesh Landscape

Debugging & Observability

Deploying: ksync, Skaffold