What is Dialogflow and what are its top alternatives?
Top Alternatives to Dialogflow
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. ...
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. ...
It combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a "question answering" machine. ...
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. ...
Telegram Bot API
Bots are third-party applications that run inside Telegram. Users can interact with bots by sending them messages, commands and inline requests. You control your bots using HTTPS requests to our bot API. ...
With bots and live-messaging tools, you can create a custom experience for your unique audience. ...
API.AI is a natural language understanding platform that makes it easy for developers to design and integrate intelligent, robust conversational user interfaces into mobile, web applications, and devices. ...
It is an open source developer tool for building chat bots, apps and custom integrations for major messaging platforms. ...
Dialogflow alternatives & related posts
- Easy console7
- Built in chat to test your model4
- Easy integration2
- Great voice2
- English only3
related Amazon Lex posts
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.
- Well documented, easy to use16
- Sending Proactive messages for the Different channels2
- LUIS feature adds multilingual capabilities1
related Microsoft Bot Framework posts
- Prebuilt front-end GUI1
- Intent auto-generation1
- Custom webhooks1
related IBM Watson posts
- High Performance24
- Connect Research and Production16
- Deep Flexibility13
- True Portability9
- Easy to use2
- High level abstraction2
- Hard to debug5
- Documentation not very helpful1
related TensorFlow posts
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:
(Direct GitHub repo: https://github.com/uber/horovod)
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.
- Integrating with other services9
- Getting customized notifications and news7
- Creating custom tools like GitHub bot5
- Easy setup4
- Creating private/public bots4
- Great documentation which is easily understandable4
- Easily manageable1
related Telegram Bot API posts
- Clean and Simple Communication1
related Messenger Platform posts