Transforming telecom customer experience with AI

In this webinar, we will dive into the evolution of ML platforms. We shed light on how to identify and define the right business use cases for AI deployment. The challenge for enterprises is to select the right tools often ranging from open-source, fit-to-purpose applications to end-to-end ML workbench. We will attempt to demystify the hype and provide our view on building ML applications with real-world examples.

In artificial intelligence (AI), increasingly complex algorithms, larger datasets, and process-intensive workloads lend to an insatiable demand for compute, memory, and storage, as well as higher-bandwidth, lower-latency communication between these components. Conversational AI, recommender systems, and computer vision are becoming increasingly prevalent, with new system architectures being explored to enable AI models with hundreds of trillions of parameters, and power supercomputers beyond exascale. These advances are bringing new challenges as the physical limitations of standard electrical interconnects are nearing the point of diminishing returns. Performance and efficiency losses from memory capacity limitations, network bottlenecks and stranded resources are being amplified at scale, creating a need for new system architectures to handle ever-growing, performance-intensive workloads.

Why and When do we need MLOps?, AWS MLOps Framework, AWS MLOps Solutions Implementation : Different approaches, An industry use case showing the implementation.

Want to learn how artificial intelligence and machine learning (AI/ML) can further improve your business? In this webinar you will hear how to: Connect AI/ML to your data both on-premises and in the cloud. Use popular data analysis and ML distributions and frameworks such as Anaconda, scikit-learn, OpenCV, and TensorFlow. Easily connect these tools and frameworks to the Oracle products holding your data.