API World + AI DevWorld 2021

In 2020 the worldwide annual cost of API development reached 100bn USD. As the global pandemic further accelerated the push for digital transformation, the need for connecting business digitally reached the all-time maximum. Yet, we are still manually wiring our systems together. We hard-code our applications in a process hardly scalable and borderline reliable.The autonomous integration pattern enables applications to discover, contract, and connect automatically without worries about maintenance. Private and public registries of business capabilities will form the backbone of Autonomous Integration Mesh and replace word-of-mouth and web search. Self-navigating and self-healing API clients will reduce the need for tedious work and provide blazing-fast resilient connections. Finally, API clients will contract and purchase digital capabilities opening the new era of all-digital sales and AI trading.This talk will explore autonomous API integration and discuss its practical implementation, cost, and time reduction impact on current API practices.

In this session we will see how to assign a phone number to a chatbot created using Dialogflow, Google Cloud Platform, Node.Js and the Vonage API integrations. The architecture shown will allow the user to call your agent by phone with a user experience similar or equal to that possible via the web.You can use Dialogflow and Google Cloud Platform for many reasons, we can create interactions to be used within your own communities, may it be a conversational application for families, companies, sports; to help workflows for both customers and businesses. Sometimes it can be a bad thing to talk to an automated conversation, if it is not well-designed.These pieces of technology can also help you escalate the conversation to a real human, as it can help you detect when human intervention is needed by using the ability of sentiment analysis, leveraging both sides of AI and Machine Learning in one computer-human interaction platform!

Today, data is being generated from devices and containers living at the edge of networks, clouds and data centers. We need to run business logic, analytics and deep learning at the edge before we start our real-time streaming flows. Fortunately using the all Apache FLiP stack we can do this with ease! Streaming AI Powered Analytics From the Edge to the Data Center is now a simple use case. With MiNiFi we can ingest the data, do data checks, cleansing, run machine learning and deep learning models and route our data in real-time to Apache NiFi and/or Apache Pulsar for further transformations and processing. Apache Flink will provide our advanced streaming capabilities fed real-time via Apache Pulsar topics. Apache MXNet models will run both at the edge and in our data centers via Apache NiFi and MiNiFi.