Today, production support is a critical part of any business, and the most critical part of the production support process is a proactive resolution of the issues faced. Identifying problems at the earliest is no less than a necessity. Therefore, continuous monitoring and identification of issues, their resolution, and improvements should be the focal point of a production support process.
The production support team should be able to identify the issues, their impact on the business and the mitigating steps it can take to avoid the same issue again. The implementation of this process of identification and mitigation should be in such a manner that it becomes a part of the normal functioning of the organization.
In production support processes there are a number of tasks that need to be made, and can easily be made simpler through automation.
Not having the ability to identify or predict where there is going to be a delay, your actions are going to be reactive, not proactive. And since it will take more time to resolve, the production time will also increase which will eventually impact your end-user satisfaction.
How automation would bring efficiency and timeliness to the entire production process?
Using machine learning to accurately predict daily ETA for critical applications to track daily loads. It helps the support team and stakeholders get a notification of any delay in the ETA. Being informed about the delays will allow the team to take preventive and proactive actions.
In this article, we will talk about how machine learning is used to monitor, identify, and improve the prediction efficiency of production completion time with the usage of a suitable ML model.
Let’s have a look at what are the most common challenges found in Production Support.
Monitoring and tracking service level agreements (SLAs) can be a challenge for organizations. With hundreds of services, each with its own SLA, how do you keep track of them all? Keeping SLAs up-to-date is challenging as it is essential. Would you risk penalties or delays if an SLA fell short or became unavailable during a critical project? With so many services, it’s easy to lose track of what’s required. It can result in missed deadlines, missed expectations, and penalties for unavailability that, in some cases, even result in program termination.
Manual processes are time-consuming and based on assumptions, making them prone to human error.
In a large enterprise setup, SLA tracking can be challenging owing to the sheer volume of jobs having SLA and multiple associated predecessor processes. Multiple factors like delayed source file arrival or failed predecessor jobs contribute to processing delays. With the addition of new features, the number of jobs with SLA and associated dependencies will continue to grow, adding to monitoring and tracking complexity. Manual monitoring would be cumbersome and prone to human error, leading to delays in issue identification and subsequent resolution resulting in the missed deadline and end-user satisfaction.
There are numerous challenges businesses face in the near real-time prediction of on-time delivery of products.
To succeed in production, organizations need to be able to predict when a specific job is finished in the most efficient and economical way possible. This involves using Machine Learning that can identify patterns in historical data and make more near real-time predictions as per how variables impact the completion time of production jobs.
How do we at Bitwise go about building an effective solution?
With the adoption of AIML, we have created an effective solution that predicts any potential delays, thereby ensuring critical applications receive the attention they deserve.
The daily stats such as file or data arrival time at the data warehouse or data lake and the time-to-complete initial stages helps us determine any irregular activities and adjust predictions accordingly. It also helps in managing the ongoing workload by identifying bottlenecks and delays in data processing.
Our model uses patterns such as weekends, public holidays, trends, and data volumes to co-relate its impact on the total execution time of pipelines and accordingly predict completion time. Model evaluation is an integral part of the solution.
Different solution strategies and algorithms (like Logistic Regression, Random Forest, Support Vector Machine etc.) are effectively evaluated to choose the best algorithms based on evaluation metrics like RSME and R-Squared values
With the help of training, the ML model can understand and learn the patterns in the data to
efficiently help predict delays.
Our machine learning models use historical data to extract relevant factors contributing to the total execution time of pipelines. The system health parameters are also considered to monitor the daily job progress and predict data load completion for applications. With this tool, production support teams will always have a predicted ETA. This factor will ensure that they are always on top of their production schedule and that their processes are running as efficiently as possible.
Further, with the end-to-end pipeline, day-to-day predictions of the production pipeline can be made, keeping support staff updated on anticipated production times. A streamlined production process will help to keep products on schedule with minimal or no errors.
By training the machine learning support model, organizations can effectively reduce the time and resources spent on planning and predicting production timelines.
With the assistance of the Machine learning model, the support team no longer needs to spend time actively monitoring the ETL pipelines. They can leverage the proactive delay predictions to identify issues and resolve them quickly ensuring minimal impact on data availability. This enables the team to focus on continuous improvement to make the overall support model more efficient.
Readiness: Support staff is always notified
Machine learning is used to create a powerful tool that can help organizations predict the completion time of production jobs. After training your support model, it will effectively enable support teams to get the prediction with maximum accuracy. Through the usage of chatbots for effective notification, the model keeps support teams always updated with predicted ETA.
It is important to consistently work on configuring changes to maintain the accuracy of the model.
Machine learning models have to be monitored over a period of time and with every change happening in the production setup the accuracy of the model can take a hit. Therefore, periodically monitoring the results and making configuration changes in the model is required.
Confidence: Proactively identifying problems allows us to address issues before they appear to the customer.
Analyzing the production data can help identify the performance of jobs in the pipeline, allowing for continuous improvements in the process. By proactively identifying problems before they appear to the customer, companies can address them in a timely and effective manner. This allows us to maintain the customer’s trust, which is essential in today’s highly competitive market. Utilizing machine learning algorithms to identify patterns in data, one can quickly and accurately identify problems. This allows us to take action before the customer has a chance to report the issue.
The solution model coupled with an impressive interface and visually intuitive reports helps realize the prediction data and take requisite actions as needed.
With the help of Machine Learning, we can predict not only the completion time of the product but also the relevant factors that contribute to it. This can help us in better planning and execution of workflows.
So, is the machine learning support model a silver bullet to comprehensive service inventory management? No, it’s just another tool that can be leveraged in your already-growing tool belt. Collecting data and patterns in your services can help you predict the completion of a job that needs to be done within a certain time frame. If you have it available in your Service Level Agreements (SLAs), this type of prediction can greatly pay off.