Alternatives to IBM Watson logo

Alternatives to IBM Watson

Amazon Lex, Amazon Comprehend, Dialogflow, Microsoft Bot Framework, and TensorFlow are the most popular alternatives and competitors to IBM Watson.
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What is IBM Watson and what are its top alternatives?

It combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a "question answering" machine.
IBM Watson is a tool in the Chatbot Platforms & Tools category of a tech stack.

Top Alternatives to IBM Watson

  • Amazon Lex
    Amazon Lex

    Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions. ...

  • Amazon Comprehend
    Amazon Comprehend

    Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to discover insights from text. Amazon Comprehend provides Keyphrase Extraction, Sentiment Analysis, Entity Recognition, Topic Modeling, and Language Detection APIs so you can easily integrate natural language processing into your applications. ...

  • Dialogflow
    Dialogflow

    Give users new ways to interact with your product by building engaging voice and text-based conversational apps. ...

  • Microsoft Bot Framework
    Microsoft Bot Framework

    The Microsoft Bot Framework provides just what you need to build and connect intelligent bots that interact naturally wherever your users are talking, from text/sms to Skype, Slack, Office 365 mail and other popular services. ...

  • TensorFlow
    TensorFlow

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...

  • Oracle
    Oracle

    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database. ...

  • HubSpot
    HubSpot

    Attract, convert, close and delight customers with HubSpot’s complete set of marketing tools. HubSpot all-in-one marketing software helps more than 12,000 companies in 56 countries attract leads and convert them into customers. ...

  • Alexa
    Alexa

    It is a cloud-based voice service and the brain behind tens of millions of devices including the Echo family of devices, FireTV, Fire Tablet, and third-party devices. You can build voice experiences, or skills, that make everyday tasks faster, easier, and more delightful for customers. ...

IBM Watson alternatives & related posts

Amazon Lex logo

Amazon Lex

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Build conversational voice and text interfaces, using the same deep learning technologies as Alexa
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PROS OF AMAZON LEX
  • 7
    Easy console
  • 4
    Built in chat to test your model
  • 2
    Easy integration
  • 2
    Great voice
CONS OF AMAZON LEX
  • 5
    English only

related Amazon Lex posts

Arthur Boghossian
DevOps Engineer at PlayAsYouGo · | 3 upvotes · 70.2K views

For our Compute services, we decided to use AWS Lambda as it is perfect for quick executions (perfect for a bot), is serverless, and is required by Amazon Lex, which we will use as the framework for our bot. We chose Amazon Lex as it integrates well with other #AWS services and uses the same technology as Alexa. This will give customers the ability to purchase licenses through their Alexa device. We chose Amazon DynamoDB to store customer information as it is a noSQL database, has high performance, and highly available. If we decide to train our own models for license recommendation we will either use Amazon SageMaker or Amazon EC2 with AWS Elastic Load Balancing (ELB) and AWS ASG as they are ideal for model training and inference.

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Amazon Comprehend logo

Amazon Comprehend

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Discover insights and relationships in text
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PROS OF AMAZON COMPREHEND
    Be the first to leave a pro
    CONS OF AMAZON COMPREHEND
    • 2
      Multi-lingual

    related Amazon Comprehend posts

    Dialogflow logo

    Dialogflow

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    Give users new ways to interact with your product by building engaging voice and text-based conversational apps.
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    PROS OF DIALOGFLOW
    • 14
      Built-in conversational agents
    • 6
      Custom Webhooks
    • 4
      OOTB integrations
    • 4
      Great interface
    • 3
      Multi Lingual
    • 2
      Knowledge base
    • 1
      Quick display
    CONS OF DIALOGFLOW
    • 8
      Multi lingual
    • 2
      Can’t be self-hosted

    related Dialogflow posts

    Microsoft Bot Framework logo

    Microsoft Bot Framework

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    Connect intelligent bots that interact via text/sms, Skype, Slack, Office 365 mail and other popular services
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    PROS OF MICROSOFT BOT FRAMEWORK
    • 18
      Well documented, easy to use
    • 3
      Sending Proactive messages for the Different channels
    CONS OF MICROSOFT BOT FRAMEWORK
    • 1
      LUIS feature adds multilingual capabilities

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    Dear All,

    We are considering Chat BOT implementation. However, we are not sure which tool gives what features and when we need to choose. (listing, comparison of Microsoft Bot Framework Vs Power Virtual Agents) Can you please provide the same?

    See more
    TensorFlow logo

    TensorFlow

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    Open Source Software Library for Machine Intelligence
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    PROS OF TENSORFLOW
    • 26
      High Performance
    • 16
      Connect Research and Production
    • 13
      Deep Flexibility
    • 9
      Auto-Differentiation
    • 9
      True Portability
    • 3
      High level abstraction
    • 2
      Powerful
    • 2
      Easy to use
    CONS OF TENSORFLOW
    • 9
      Hard
    • 6
      Hard to debug
    • 1
      Documentation not very helpful

    related TensorFlow posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.4M views

    Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

    At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

    TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

    Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

    https://eng.uber.com/horovod/

    (Direct GitHub repo: https://github.com/uber/horovod)

    See more

    In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

    Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

    !

    See more
    Oracle logo

    Oracle

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    An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
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    PROS OF ORACLE
    • 42
      Reliable
    • 31
      Enterprise
    • 15
      High Availability
    • 5
      Expensive
    • 5
      Hard to maintain
    • 4
      Maintainable
    • 3
      High complexity
    • 3
      Hard to use
    CONS OF ORACLE
    • 13
      Expensive

    related Oracle posts

    Hi. We are planning to develop web, desktop, and mobile app for procurement, logistics, and contracts. Procure to Pay and Source to pay, spend management, supplier management, catalog management. ( similar to SAP Ariba, gap.com, coupa.com, ivalua.com vroozi.com, procurify.com

    We got stuck when deciding which technology stack is good for the future. We look forward to your kind guidance that will help us.

    We want to integrate with multiple databases with seamless bidirectional integration. What APIs and middleware available are best to achieve this? SAP HANA, Oracle, MySQL, MongoDB...

    ASP.NET / Node.js / Laravel. ......?

    Please guide us

    See more
    HubSpot logo

    HubSpot

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    All the software you need to do inbound marketing.
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    PROS OF HUBSPOT
    • 42
      Lead management
    • 18
      Automatic customer segmenting based on properties
    • 16
      Email / Blog scheduling
    • 0
      Any Franchises using Hubspot Sales CRM?
    CONS OF HUBSPOT
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      HubSpotHubSpotPipedrivePipedrive

      Looking for the best CRM choice for an early-stage tech company selling through product-led growth to medium and big companies. Don't know if Salesforce or HubSpot are too rigid for PGL and expensive. I also had an experience of companies outgrowing Pipedrive pretty fast

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      Alexa logo

      Alexa

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      A cloud-based voice service
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      PROS OF ALEXA
        Be the first to leave a pro
        CONS OF ALEXA
          Be the first to leave a con

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