Operations | Monitoring | ITSM | DevOps | Cloud

When you want to stay ahead and on top of things in a fast-moving industry, machine learning (ML) is surely one of the trending solutions. Today, innovative companies already have leading Machine Learning tools well-integrated into their processes. In comparison, your start could seem dreadfully slow. Or maybe you just don’t have the time or resources to invest in running your own Machine Learning training infrastructure.

For time series workloads the ability of serverless functions to scale up and down is a major advantage, especially for something like IoT devices that may have intermittent connectivity and might suddenly send data in bursts. In this type of situation, it doesn’t make sense to be paying for a server to be running 24/7 when you can use a serverless function and only pay for the compute you use.

Azure Data Factory is a cloud-based data integration service that allows you to orchestrate and automate data movement and transformation using cloud-based data-driven workflows. It provides a cost-effective solution whenever a dependable code-free ETL tool on the cloud with numerous integrations is required. This blog will highlight how users can use Serverless360 to manage and monitor multiple Azure Data Factories with different pipelines. Azure Data Factory consists of a variety of components like.