So far, we have thoroughly explored and understood all the necessary prerequisites essential for our project. This foundational knowledge has equipped us to move forward effectively. Following this, we have successfully developed a machine learning model that is capable of predicting machine readiness. This model serves a critical role in our overall system, as it utilizes historical data and various algorithms to assess the operational status of the machinery and forecast potential downtimes. Furthermore, we have taken the next step by creating several distinct functions, each encapsulating specific logic and processes that contribute to the functionality of our model. These functions are designed to handle various tasks such as data preprocessing, feature extraction, model training, and evaluation, ensuring that our approach is both modular and efficient.
At this juncture, it is imperative that we integrate all these components into a cohesive system. Therefore, our next objective is to combine all the individual functions and the machine learning model into a single job or workflow. This job will orchestrate the execution of all the processes in a seamless manner, allowing us to automate the prediction of machine readiness. By doing so, we will enhance the efficiency of our operations and ensure that the model can be deployed effectively in a real-world setting. This integration will not only streamline the workflow but also facilitate easier maintenance and updates in the future. Ultimately, this step is crucial for transitioning from a theoretical framework to a practical application that can deliver tangible results.
First of all we need to schedule resource scheduler which will invoke our Intelligent vulnerability function on daily bases.
I already pre-created scheduler and it is executing on daily bases.
This scheduler invokes my function and created patch update job.
Let's check GenAI predict logs.
Successfully predicts value for all 4 VM's.
This function create two copies of 2nd function which triggers patch update job.
It also created two resource scheduler to invoke these functions.
Let's validate instances:
Boot Volume backup created
Let's check OSMH job
Check for 2nd Instance:
So both instances patch update successfully, demonstrating the effectiveness of our current patch management strategy. This successful update not only ensures that the instances are running the latest software versions, which often include critical security fixes and performance improvements, but it also highlights the reliability of our infrastructure in handling updates without any downtime. The seamless execution of these updates is crucial for maintaining operational efficiency and minimizing risk across our systems.
So this is just one example of the capabilities we can harness using the OCI Data Science module, which is designed to facilitate advanced data analysis and machine learning applications. Beyond this instance, we have access to a plethora of other tools and resources within the OCI ecosystem, including Functions, which allow us to run code in response to events without provisioning or managing servers, and the Resource Scheduler, which helps us automate the management of our cloud resources efficiently. These tools collectively empower us to build robust data-driven applications, streamline our workflows, and optimize resource utilization, ultimately enhancing our overall productivity and innovation in various projects.
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