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Machine learning (ML) models are increasingly becoming integral components of modern applications, and there is a growing need to deploy them in real-time environments. This approach empowers organizations to process and act on data in real-time allowing for immediate insights and rapid response to changing circumstances. By combining the real-time data streaming capabilities of Apache Kafka with the powerful distributed computing capabilities of Apache Spark, organizations can build robust and scalable real-time ML pipelines. However, combining Kafka and Spark poses significant challenges, especially when it comes to achieving low latency and high scalability. In this webinar, you will learn: - The fundamentals of real-time ML. - The biggest challenges facing Data teams. - The optimal usage of Apache Kafka and Apache Spark and their unique strengths and capabilities in the context of real-time data processing and analysis Who should attend: - Cloud, Software, and Data Architects - Big Data and ML Engineers