![]() The end result of that phenomenon is a large ecosystem of open source projects. At different points in time, the founding company (or companies) of such an effort decide to open source projects as a consequence of wanting to build an ecosystem around it and to collaborate with others on constructing a platform. Most open source AI projects started as proprietary efforts and are the result of years of investment and talent acquisition. The LF AI landscape is an interactive tool that shows both how fragmented the space as well as the wide range of projects in each technology category. To help developers and data scientists make sense of the diversity of projects, the LF AI landscape (Figure 1) was originally published in December 2018 and has been continuously updated ever since. ![]() While there are many applications and tools out there, the integration between them can be complicated, can pose additional challenges especially in relation to long term sustainability, and may present a barrier for and adoption as part of a commercial product/service. ![]() Use and adoption of Machine Learning (ML) and Deep Learning (DL) technologies is exploding with the availability of dozens of such open source libraries, frameworks and platforms, not to mention all the proprietary solutions. Ofer Hermoni, Director of Product Strategy in Amdocs’ CTO office,
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