How I Trained a Chatbot K.AI of Myself Without Coding Evaluating OpenAI Custom GPT Chatbase Botsonic LiveChatAI
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Hello, K.AI – How I Trained a Chatbot of Myself Without Coding Evaluating OpenAI Custom GPT, Chatbase, Botsonic, LiveChatAI

Generative AI (GenAI) enables many new use cases for enterprises and private citizens. While I work on real-time enterprise scale AI/ML deployments with data streaming, big data analytics and cloud-native software applications in my daily business life, I also wanted to train a conversational chatbot for myself. This blog post introduces my journey without coding to train K.AI, a personal chatbot that can be used to learn in a conversational pace format about data streaming and the most successful use cases in this area. Yes, this is also based on my expertise, domain knowledge and opinion, which is available as  public internet data, like my hundreds of blog articles, LinkedIn shares, and YouTube videos.
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The Past Present and Future of Stream Processing
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The Past, Present and Future of Stream Processing

Stream processing has existed for decades. The adoption grows with open source frameworks like Apache Kafka and Flink in combination with fully managed cloud services. This blog post explores the past, present and future of stream processing, including the relation of machine learning and GenAI, streaming databases, and the integration between data streaming and data lakes with Apache Iceberg.
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Apache Kafka + Vector Database + LLM = Real-Time GenAI

Generative AI (GenAI) enables advanced AI use cases and innovation but also changes how the enterprise architecture looks like. Large Language Models (LLM), Vector Databases, and Retrieval Augmentation Generation (RAG) require new data integration patterns. Data streaming with Apache Kafka and Apache Flink processes incoming data sets in real-time at scale, connects various platforms, and enables decoupled data products.
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Data Streaming with Apache Kafka as Data Fabric for GenAI
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Apache Kafka as Mission Critical Data Fabric for GenAI

Apache Kafka serves thousands of enterprises as the mission-critical and scalable real-time data fabric for machine learning infrastructures. The evolution of Generative AI (GenAI) with large language models (LLM) like ChatGPT changed how people think about intelligent software and automation. This blog post explains the relation between data streaming and GenAI and shows the enormous opportunities and some early adopters of GenAI beyond the buzz.
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Business Process Automation and BPM Workflow Engine and Orchestration with Apache Kafka
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Apache Kafka as Workflow and Orchestration Engine

Business process automation with a workflow engine or BPM suite has existed for decades. However, using the data streaming platform Apache Kafka as the backbone of a workflow engine provides better scalability, higher availability, and simplified architecture. This blog post explores case studies across industries to show how enterprises like Salesforce or Swisscom implement stateful workflow automation and orchestration with Kafka.
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Cybersecurity in Crypto and FinTech with Data Streaming and Apache Kafka
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Apache Kafka in Crypto and FinServ for Cybersecurity and Fraud Detection

The insane growth of the crypto and fintech market brings many unknown risks and successful cyberattacks to steal money and crypto coins. This post explores how data streaming with the Apache Kafka ecosystem enables real-time situational awareness and threat intelligence to detect and prevent hacks, money loss, and data breaches. Enterprises stay compliant with the law and keep customers happy in any innovative Fintech or Crypto application.
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Machine Learning and Data Science with Apache Kafka in Healthcare
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Machine Learning and Data Science with Kafka in Healthcare

IT modernization and innovative new technologies change the healthcare industry significantly. This blog series explores how data streaming with Apache Kafka enables real-time data processing and business process automation. Real-world examples show how traditional enterprises and startups increase efficiency, reduce cost, and improve the human experience across the healthcare value chain, including pharma, insurance, providers, retail, and manufacturing. This is part five: Machine Learning and Data Science. Examples include Recursion and Humana.
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