Wisdom Udo, a seasoned data scientist, has successfully led a project to optimize network performance at BT Group, resulting in improved latency and throughput. Udo’s expertise in data science and MLOps best practices enabled the development of predictive models that ensured seamless integration into production environments.
The project aimed to optimize BT Group’s network efficiency, focusing on high latency and low throughput. The network faced congestion and uneven traffic distribution, leading to frequent bottlenecks. Udo’s team was tasked with developing a solution that could handle the complex data landscape and provide real-time insights to improve network performance.
Udo’s leadership and expertise in data science were instrumental in bringing together a cross-functional team of data engineers, data analysts, and network engineers. The team worked collaboratively to develop a comprehensive solution that integrated data science techniques with network engineering expertise.
The project involved developing automated data pipelines using Python, Kafka, and MongoDB, ensuring continuous and accurate data flow from network operations, customer interactions, and performance metrics. Spark was employed for distributed processing, and Airflow was used to orchestrate data-cleaning tasks such as outlier detection, data imputation, and feature scaling.
The data science team developed predictive models using Python, TensorFlow, and XGBoost, leveraging MLflow for experiment tracking and hyperparameter tuning. The models were designed to detect anomalies and predict network congestion, enabling proactive measures to prevent bottlenecks.
Udo’s team ensured that the models were scalable and reliable, with continuous monitoring and logging using Prometheus and Grafana. The ELK Stack was used for detailed logging and analysis, enabling continuous performance monitoring and anomaly detection.
Real-time visualization dashboards were set up using Tableau, providing stakeholders with actionable insights into network performance. The dashboards enabled network engineers to quickly identify areas of congestion and take corrective action, resulting in improved network performance and reduced downtime.
Key performance indicators showed significant improvements, with network latency reduced by 11% and throughput increased by 15%. Downtime frequency decreased by 25%, and mean time to recovery was reduced by 25%, resulting in a 21% rise in positive customer feedback.
BT Group’s network management technology is now considered industry-leading, thanks to Udo’s expertise in data science and MLOps. The project demonstrates the power of data science in optimizing complex systems and improving customer experience.
Udo’s achievement is a testament to his skills and expertise in leading cross-functional teams and developing predictive models that drive business results.
His contributions to the field of data science are truly commendable, and his work at BT Group is an inspiration to data scientists and network engineers alike.
and then