Hello everyone. This is Sunil Kumar and I work in AI data engineering. Today I want to talk about using go for realtime NLP in ai, introduction to NLP and realtime processing. NLP is a crucial component in many time AI applications, but it comes with its own set of challenges in terms of latency. Scalability and efficiency. Understanding these challenges in first step in leveraging the right tools, techniques to build high performance NLP Powered Solutions while using go for NLP Go compile nature concurrency support, scalability and memory efficiency, making it a powerful choice for real time. NLP processing in AI applications. Key NLP libraries in Go Spago Pros, NLP Tensor Flow Go API Go feed use cases for realtime NLP with Go. We have five use cases, chat bot and AI assistance, sentiment analysis, real time streaming, Kafka go. Spam filtering and content moderation was to text NLP pipelines how Go Handles real-time NLP efficiently go. Unique feature like goin low memory usage and integration with streaming platform, making it a powerful choice for building high performance for real-time. NLP applications. Implementing real-time text processing in go. By leveraging the strength in concurrency performance and integration with streaming platform developers can build efficient real-time NLP processing pipelines to handle a wide range of text processing. Task a sample code go routine for parallel. NLP. This slide explains how goin can be used for parallel processing of NLP task by leveraging concurrency. Goins allows for efficient utilization of a system, resources, and faster processing of text data. Integrating go with Kafka for real time. NLP. Kafka Streaming provides a realtime data processing platform that can be leveraged to consume and process the text data for NLP Task. By integrating, go with Kafka, developers can build scalable high performance pipelines that can be process streaming data in real time. Go routines and channels allows for efficient parallel processing of messages. While low memory footprint. The language ensures that the NLP workloads can handle high throughput with performance degradation. Deploying NLP models in go with 10 share flow APIs. Deploying pre-trained NLP models in GO is a made possible through the 10 flow go API. This allows developer to seamlessly integrate powerful mission learning models into their GO applications. Enabling advanced NLP capabilities by leveraging the 10 flow go API go developers access wide range of. Pre trained NLP models and deploy them within their realtime processing pipelines. The future of NLP with GO looks promising this potential for an extended ecosystem, deeper integrations with AI framework and increased adoption in real time applications as a GO community continues to. We can expect to see more innovative solutions and use cases for leveraging the language unique strength in NLP domain. The best practice for go in realtime NLP by following these best practice. Developer can leverage the strength of GO programming language to build high performance, scalable and efficient real-time NLP solutions, a variety of AI applications. Performance comparison, go versus Python. For NLP feature, speed go is faster. Concurrency. Python has limited ability to handle multitask at once while go Does it better? NLP libraries. Python has lot of NLP libraries, but GO has fewer options. Memory, usage, Python use. More memory than Go. Scalability. Go is better than Python when it comes to scaling. Challenges of using go for NLP while go is a strength in speed, concurrency and memory efficient, making it promising choice for real-time NLP. The current limitations in the GO NLP ecosystem needs to be addressed through continued library development and community growth. The Go programming language is promising choice for NLP processing in AI applications, offering significant advantages in the terms of performance scalability while NLP ecosystem in GO is still maturing the language core strength and integration with other tools, making it compelling option for developers. Perform solution. Thank you so much for this great opportunity. I hope you guys enjoyed my conversation. Thank you.