Machine data: how to handle it better?

The rise of IoT and smart infrastructure has led to the generation of massive amounts of complex data. Traditional solutions struggle to cope with this shift, leading to a decrease in performance and an increase in cost. In this talk, we will take a look at this kind of data using a simulated Curiosity rover. Participants will learn how to create a data pipeline for ingestion and visualisation. By the end of this session, we will be able to set up a highly scalable data pipeline for complex time series data with real time query performance.

 
 

Outline/Structure of the Talk

High level outline of topics that will be covered in this presentation:

1. Growth of IoT and Sensor Data

2. Time-series data

3. Challenges that are posed by large volumes of time-series data

4. Showcasing and overcoming the problem: A case-study

5. Demo time: Curiosity rover simulation and data ingestion from the sensors and visualisation

Learning Outcome

By the end of this session, we will be able to set up a highly scalable data pipeline for complex time series data with real time query performance.

Target Audience

Developers, Managers, IoT Specialists

Prerequisites for Attendees

Some knowledge of databases, data pipelines and containers will help the audiences to follow along and make the most of this talk.

schedule Submitted 10 months ago

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    Shama Ugale - Testing your Bot!

    Shama Ugale
    Shama Ugale
    Sr. QA Consultant
    ThoughtWorks
    schedule 1 year ago
    Sold Out!
    45 Mins
    Talk
    Beginner

    Chatbots are one of the most widely adopted AI/ML implementations in the business sector. A chatbot is an intelligent machine used to imitate human conversation through text and voice commands. Today bots are widely used as a personal assistant, customer service, HR, sales and marketing to name a few. In short, bots are everywhere and we rely on them to a certain extent, this makes it extremely important to assure the quality of the chatbots and test them thoroughly. They are built using NLU/NLP-Services (Natural language understanding and processing) and are subjected to constant training and improvement which has direct impact on tests. Voice based bots like Siri and Alexa depend on speech recognition technologies. As the chatbots user do not have any barriers and due to the unpredictable user’s behavior it becomes utmost difficult to verify the correctness on the output. In this talk, we will discuss how the chatbots are different as compared to the other applications and the challenges they bring onto the table while verifying their behavior, and focus on the testing strategies and automation testing of the bots.