![]() The model will automatically get retrained on a certain schedule in order to stay up-to-date on the latest data. To give you an example, let’s assume you defined a machine learning model that is continuously applied to new data to provide an analyst with some results. This is where Splunk’s Data-To-Everything Platform can play to its full strength in providing you with answers to all sorts of questions you might have around your machine learning operations. You may also want to lower your service costs or optimize your infrastructure as well as your actual model code making it more robust or run more efficiently. you face a severe model degradation or your data quality for (re)training or inference changed dramatically. In addition, you can also continuously monitor all that’s happening and proactively get alerted on deviations from the expected behavior of your ML system, e.g. Those data sources enable you to perform root cause analysis during development and in production, allowing you to analyze what exactly occurs when things go wrong or break. When you build and run a machine learning system in production, you probably also rely on some (cloud) infrastructure, components, services, and application code which provide various logs and metrics. The NIPS paper, “Hidden Technical Debt in Machine Learning Systems” summarizes this concept in the figure below:Īs a market leader in IT Operations, Splunk is widely used for collecting logs and metrics of various IT components and systems such as networks, servers, middleware, applications and generally any IT service stack. ![]() The real challenge in production grade machine learning systems is not always the actual ML code itself, but more to do with the surrounding components that make up the whole system. The term “MLOps” derives from the words machine learning (ML), development (DEV) and operations (OPS) and was coined to describe a “practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) lifecycle” according to Wikipedia. This is where you most likely start learning about various aspects of Machine Learning Operations (MLOps). You might also want to receive alerts in case of any unexpected behavior or inconsistencies with your model or your data quality. If you like it.Once you’ve reached the point where you want to deploy your machine learning models to production, you will eventually need to monitor operations and performance. It can act like a superb applicaiton monitoring and transaction monitoring product as well.įeel free to let me know you feedback through comments. Splunk also having the monitoring facilities as it can trigger email on certain conditions. Splunk search can present data as a chart (or) as a statistics (or) as a report and many more. Splunk has its own query language called SPL. For more information regarding SPL click here Data will get indexed and you are ALL SET to be amazed with Search Processing Language of Splunk and its capabilities. ![]() ( Click on review button on the top to review) ![]() Now its a time to review and submit our configuration. A single index can have multiple sources and source types. Here you can refer the index as the normal DataBase(RDBMS) index where your data is going to be stored. Now we have created the sourcetype and index named tomcat_logs. Except the name, leave all the fields _blank
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