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Caffe Deep Learning Framework vs. EDB Postgres Advanced Server

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Caffe Deep Learning Framework

    Score7 out of 10
    N/ACaffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research and by community contributors.N/A

    EDB Postgres Advanced Server

    Score8 out of 10
    N/AThe EDB Postgres Advanced Server is an advanced deployment of the PostgreSQL relational database with greater features and Oracle compatibility, from EnterpriseDB headquartered in Bedford, Massachusetts.N/A
    Pricing
    Caffe Deep Learning FrameworkEDB Postgres Advanced Server
    Editions & Modules
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    Offerings
    Pricing Offerings
    Caffe Deep Learning FrameworkEDB Postgres Advanced Server
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Best Alternatives
    Caffe Deep Learning FrameworkEDB Postgres Advanced Server
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    User Ratings
    Caffe Deep Learning FrameworkEDB Postgres Advanced Server
    Likelihood to Recommend
    4.0
    (1 ratings)
    10.0
    (2 ratings)
    User Testimonials
    Caffe Deep Learning FrameworkEDB Postgres Advanced Server
    Likelihood to Recommend
    Open Source
    Caffe is only appropriate for some new beginners who don't want to write any lines of code, just want to use existing models for image recognition, or have some taste of the so-called Deep Learning.
    Incentivized
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    EnterpriseDB
    It's great if you are using or wish to use PostgreSQL and need the added performance optimization, security features and developer and DBA tools. If you need compatibility with Oracle it's a must-have. There are many developer features that greatly assist dev teams in integrating and implementing complex middleware. It's great for optimizing complex database queries as well as for scaling. I would recommend Postgres Plus Advanced Server for any software development team that is hitting the limit of what PostgreSQL is capable of and wants to improve performance, security, and gain extra developer tools.
    Incentivized
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    Pros
    Open Source
    • Caffe is good for traditional image-based CNN as this was its original purpose.
    Incentivized
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    EnterpriseDB
    • PPAS Oracle compatibility, especially the PL/SQL syntax, has made migrating database-tier code very simple. Most Oracle packages do not need to be changed at all and those that do are generally for simple reasons like a reserved word in PPAS that is allowed in Oracle.
    • PPAS xDB, the multi-master replication tool, is simple and - most important - does not break with network or other interruptions. We have been able to configure and forget, which our customers could never do with other multi-master tools.
    • Most people had no idea that PPAS and PostgreSQL have full CRUD support for JSON. They think you need a specialized product and/or that JSON is read-only. Every organization that I have worked with is evaluating adding JSON to their relational model.
    Incentivized
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    Cons
    Open Source
    • Caffe's model definition - static configuration files are really painful. Maintaining big configuration files with so many parameters and details of many layers can be a really challenging task.
    • Besides imagine and vision (CNN), Caffe also gradually adds some other NN architecture support. It doesn't play well in a recurrent domain, so we have to say variety is a problem.
    • Caffe's deployment for production is not easy. The community support and project development all mean it is almost fading out of the market.
    • The learning curve is quite steep. Although TensorFlow's is not easy to master either, the reward for Caffe is much less than the TensorFlow can offer.
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    EnterpriseDB
    • Documentation is excellent but spread out across many resources and can take a while to wade through—would benefit from having more intro level, getting started guides for various languages.
    • Ruby support is excellent but more Ruby examples and beginner-level documentation would be nice.
    • It is sometimes hard to find a community of users on StackOverflow so a larger community, and a dedicated forum with active members to answer questions and work through issues would be nice.
    Incentivized
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    Alternatives Considered
    Open Source
    TensorFlow is kind of low-level API most suited for those developers who like to control the details, while Keras provides some kind of high-level API for those users who want to boost their project or experiment by reusing most of the existing architecture or models and the accumulated best practice. However, Caffe isn't like either of them so the position for the user is kind of embarrassing.
    Incentivized
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    EnterpriseDB
    PPAS proved better for our customer's data-centric apps than Oracle in all but a few edge cases (encryption at rest and multi-TB database-tier backups) because it is simpler to install/maintain, runs nearly all Oracle-syntax SQL as well as ANSI SQL. PPAS has much more JSON capabilities (full CRUD vs. read-only in Oracle), simpler geospatial, simpler / more stable replication and datatypes that match developer expectations, such as BOOLEAN and ENUMs.
    Incentivized
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    Return on Investment
    Open Source
    • Since we stopped using Caffe before it can reach the production phase, there is no clear ROI that can be defined.
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    EnterpriseDB
    • Postgres Plus Advanced Server is quite complex and may take longer to implement certain things than simply using PostgreSQL depending on developer familiarity with the platform.
    • Getting up to speed can be daunting so again, there is an upfront cost in time spent learning the platform, besides the potential for extra time spent on a feature-by-feature basis.
    • The cost of Postgres Plus Advanced Server should be weighed against simply using PostgreSQL to decide which is the best solution for your business needs.
    Incentivized
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