ML in DevOPS, RPA and BPA

7.3. ML in DevOPS, RPA and BPA#

7.3.1. How does machine learning (ML) support automation through the use of…#

DevOps#

Recommended Video: What is DevOps? - In Simple English (7 mins)

DevOps is a collection of practices designed to close the gap between software development (Dev) and IT operations (Ops). Its main goal is to help teams develop, test, and deploy software more quickly, reliably, and efficiently. By streamlining the development process, DevOps allows software to reach the market faster, reduces the risk of deployment failures, and enables quicker recovery from issues.

Although DevOps is a practice that is independent from machine learning, there are several areas where machine learning can be integrated into the DevOps process to enhance its effectiveness.

Examples of this include:

  • Software developers using machine learning when creating automated tests. This improves their productivity and may also be used to suggest tests they may not have originally considered.

  • Machine learning can automatically monitor software performance to help teams predict errors, identify performance bottlenecks, detect security vulnerabilities, and suggest improvements, thus enabling DevOps teams to act proactively instead of reactively.

RPA#

Recommended Video: RPA In 5 Minutes | What Is RPA - Robotic Process Automation? | RPA Explained | Simplilearn (6 mins)

Robotic Process Automation (RPA) uses software to automate repetitive, rule-based tasks like data entry, form processing, or moving information between systems. These bots mimic human actions on a computer, improving speed, accuracy, and efficiency without changing existing systems. RPA reduces human error, operates 24/7, and frees up employees to focus on more valuable work.

Machine learning improves RPA by allowing it to manage more complex, unstructured, and changing tasks, making automation more intelligent and flexible.

Examples of this include:

  • Using machine learning to extend from rule-based tasks such as data entry to handling unstructured data like emails, images, or PDFs, through pattern recognition, natural language processing and classification to compile useful data.

  • Machine learning can enhance security by enabling more advanced detection of suspicious activity. For instance, while rule-based systems might flag only transactions above a certain amount, machine learning can analyse a customer’s transaction history to identify unusual behavior, allowing for more accurate and context-aware fraud detection.

BPA#

Recommended Video: What is Business Process Automation? (2 mins)

Machine learning supports automation in Business Process Automation (BPA) by making processes smarter, more adaptive, and data-driven. BPA focuses on automating entire workflows, such as onboarding new employees or handing errors reported by customers.

Machine learning enhances this by enabling systems to learn from data across these processes and make decisions without being explicitly programmed for every scenario.

Examples of this include:

  • Using machine learning to identify patterns in comprehensively compiled datasets from RPA, which can then be analysed holistically to identify anomalies that may indicate security risks or breaches of policy.

  • Improving customer outreach and communication by leveraging machine learning to personalise content, delivery and timing based on existing customer data. This leads to greater customer satisfaction and improved retention.