Load testing is essential for ensuring the performance and stability of modern large-scale systems, which must handle vast numbers of concurrent requests. Traditional load tests, often requiring extensive execution times, are costly and impractical within the short release cycles typical of contemporary software development. In this paper, we present our experience deploying MLOLET, a machine learning optimized load testing framework, at Ericsson. MLOLET addresses key challenges in load testing by determining early stop points for tests and forecasting throughput and response time trends in production environments. By training a time-series model on key performance indicators (KPIs) collected from load tests, MLOLET enables early detection of abnormal system behavior and provides accurate performance forecasting. This capability allows load test engineers to make informed decisions on resource allocation, enhancing both testing efficiency and system reliability. We document the design of MLOLET, its application in industrial settings, and the feedback received from its implementation, highlighting its impact on improving load testing processes and operational performance.