Incorporating AIML concepts in your day-to-day work can help in optimizing the process and making workflow easier. Read further to learn about Malavika’s journey with Great Learning’s PGP Artificial Intelligence and Machine Learning Course in her own words.
Starting with the idea of incorporating automation in software and processes wasn’t very easy for me. Automation tools have been around for years, but successfully introducing automation into my organization was more than just selecting this tool or that scripting language. I had to create a conscious strategy that was backed by my organization’s leaders. I needed to allocate adequate resources and treat automation as a critical line of development, complete with priorities and clear backlog and release cycles. I also needed to measure progress on an ongoing basis, with concrete metrics that show whether we were achieving our goals or not. I also was aware that automation had to be handled as a full-fledged software project with several contributors, not just a token expert on whom the whole project depends. If properly nurtured, your organization’s automation infrastructure should mature and grow to be robust, maintainable, and scalable.
Continuing the journey of AIML with Great Learning by undergoing the different problem statements, I understood the very fact that it was not just adopting tools but had to start with Identifying and treating pain points.
I looked for pain points in my Dept and, instead of starting with a huge project, went for something small, clearly defined, and well-scoped. What do you want to achieve? Do you want to replace manual efforts? Quick feedback on new builds? Better platform coverage? First, decide on the problem you’re going to tackle, and then choose the best tool for the job. Last, make sure you can demonstrate the value of your efforts with quantifiable results. Since my role is of a senior M&E specialist, I also ensured there are many ways to measure effectiveness. As such planned a lot of ML models
Creating a collaborative platform and getting everyone involved, and also maintaining a Knowledge base
Keeping any knowledge domain in the hands of one expert is risky. People change jobs, get sick, or move on to other projects. You do need a clear owner for every automation project—someone who will keep it moving toward clearly defined goals—but everyone else in your organization should also be involved. No single person should be responsible for developing and maintaining the whole automation infrastructure. I also got the confidence of writing frameworks by The PGP-Cloud Computing course I was doing parallelly with great Learning, by which I could initiate the designing of the automation architecture.
This project of automation has got Acceptance by Management to go ahead with the proposed framework and integrate ML and AI models to make it more dynamic.
