NetBeans has completed its transition to Apache and is now a top level Apache project with a strong and dedicated community and millions of users worldwide. NetBeans always had great support for Apache Maven & having an IDE of our own is a great chance to further promote Apache projects to a large audience. In this session I’ll show you how to plugin your own language, tool, library, server, database or framework and make it easy for developers to get started with them. Use NetBeans as a marketing tool to shamelessly plug your own cool project.
Does it seem strange to you that we collectively collaborate on code but training material is produced individually in private? Why would each company or person produce their own material when it can be sourced from a central location, under a business friendly license, and built on and modified? Or perhaps you just see better ways of producing content, then come along and listen to what the Apache Training project is doing. You’ll find out how make nice presentations with simple markup that can be put under version control and exported to many formats.
Availability of content and training sets is a major bottleneck for a chatbot development today. Relying on Apache OpenNLP and its sub-project OpenNLP.chatbot, we introduce a number of tools and components to design a chatbot and its training set to be knowledgeable and intelligent. n In this talk we will analyze the reasons it is so hard to find a chatbot demo today for a nontrivial task or to observe an intelligent behavior of a chatbot. It is easy to see how a success in AI can boost the chatbot development on one hand, but it is hard to detect intelligence in those chatbots that are available to the public, on the other hand. n We will present an advanced search engine for chatbots with the focus on linguistic features and discourse-level analysis for dialogue management. We will introduce a tool that builds a dialogue from an arbitrary document to form a training dataset for deep learning chatbots. We will demo a chatbot supporting virtual dialogue, where a user joins a virtual community built on the fly, whose members answer questions in this user’s current area of interest. An extended content for this talk is available in the book recently published by the speaker “Developing Enterprise Chatbots”.
With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data, and hours to train models. It’s also hard to scale, with data sets increasingly being larger than the capacity of any single server. The size of the data also makes it hard to incrementally test and retrain models in near real-time to improve results. Learn how Apache Ignite and GridGain help to address these limitations with model training and execution, and help achieve near-real-time, continuous learning. It will be explained how ML/DL work with Apache Ignite, and how to get started. Topics include:n n— Overview of distributed ML/DL including design, implementation, usage patterns, pros and consn— Overview of Apache Ignite ML/DL, including prebuilt ML/DL, and how to add your own ML/DL algorithmsn— Model execution with Apache Ignite, including how to build models with Apache Spark and deploy them in Igniten— How Apache Ignite and TensorFlow can be used together to build distributed DL model training and execution
In mid-2018 the Cassandra community committed to making Cassandra 4.0 the most stable major release of Cassandra in the project’s history. In September, the community shifted focus from feature work and development to ensuring the quality of the release. To this end, we have adopted several new approaches to testing and validation including the replaying of production traffic, code audits, and property-based testing. This talk will explore the methodologies we’ve adopted and the results of their application as well as costs of this level of commitment to testing and its benefits.