Anaconda

Anaconda by sennchi

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Anaconda

Anaconda , formerly known as Continuum Analytics, is based in Austin, Texas, U.S. It sells Anaconda Enterprise 5.0, an open-source development environment based on the interactive-notebook concept. It also provides a loosely coupled distribution environment, giving access to a wide range of open-source development environments and open-source libraries, mainly Python-based.

Anaconda's strength lies in its ability to federate and provide a central access point for a very large number of Python developers who build machine-learning capabilities continuously. However, Anaconda has little or no control over those developers' efforts in terms of quality, dependability and sustainability. Anaconda nurtures a broad developer community through Anaconda Cloud. Anaconda's position as a Niche Player reflects its suitability for seasoned data scientists fluent in Python.

STRENGTHS

• **Python and open-source support: **Anaconda provides an open-source development center active through Anaconda Cloud. The growing popularity of Python among data scientists gives Anaconda excellent visibility to developers, for whom it provides a wide range of code, libraries, notebooks and shared projects. Anaconda is the only data science vendor not only supporting but also indemnifying and securing the Python open-source community, to make the platform suitable for enterprises.

• **Flexible code integration: **Anaconda's environment has exceptionally complete and flexible capabilities for integrating Python code libraries. This is important because developers can use them to tailor Anaconda to available project and compute resources. Surveyed users praised these capabilities.

• **Active ecosystem: **Reference customers praised Anaconda's extensive, active community engagement. The community fosters technological development, cutting-edge Python code libraries and integration with other open-source data science projects. Although most libraries are still in the early stages of development, some of the most advanced capabilities are available through those beta libraries.

CAUTIONS

• **Focus on experts and minimal automation: **Anaconda targets experienced data scientists familiar with Python and notebooks. Novice Anaconda users will have difficulty finding their way through the Python "jungle." The "do it yourself" skill and attitude exhibited by typical Anaconda users does not readily accept the imposition of machine-learning automation practices. Python developers tend to automate their practices themselves, rather than rely on automation mechanisms promoted by more business-oriented audiences.

• **Lack of comprehensive sales strategy: **Although Anaconda has grown steadily, its sales team still takes the "classic" approach of converting open-source Python users to Anaconda Enterprise through online marketing and evangelism. To accelerate growth, Anaconda should tap into the community that it created and continues to foster.

• **Poorly organized documentation: **Anaconda offers an excessive amount of documentation and training materials. These badly need better organization and clarity