Скачать или смотреть From Logs to Intelligence: Building Scalable Production ML Pipelines (Day 144 MLOps) | SD Course

On Day 144, learn exactly how to architect, build, and deploy robust, production-grade Machine Learning pipelines specifically designed for real-time log intelligence and anomaly detection in complex systems. Discover strategies for high-volume log data ingestion using Apache Kafka or AWS Kinesis, ensuring efficient data streaming from diverse sources like application logs (Log4j), server logs, and security event data. Explore crucial preprocessing steps, including parsing unstructured log data, tokenization, and advanced feature engineering techniques to extract meaningful patterns for log anomaly detection, root cause analysis, and predictive maintenance. We'll delve into model development frameworks like PyTorch or TensorFlow, and discuss MLOps best practices for model training, versioning, continuous integration (CI/CD), and seamless deployment using platforms like AWS SageMaker, Google Cloud AI Platform, or Kubernetes for containerized ML services. Understand the importance of real-time model monitoring using tools like Prometheus and Grafana for performance metrics, data drift, and continuous model retraining strategies to maintain log intelligence accuracy and system observability. This session provides a practical guide for data scientists, ML engineers, and MLOps professionals seeking to operationalize machine learning for enhanced system reliability and proactive security intelligence.