Navigating the Future of Machine Learning: Demystifying the Advanced Capabilities of MLOps

CXOToday has engaged in an exclusive interview with Deepak Visweswaraiah, vice president, platform engineering, and site managing director, Pegasystems, India

1. How can organizations gain better clarity with the optimal utilization of ML?

• Ever since digitalization took center stage, various modern technologies have come to the fore and benefited sectors in a dramatic manner. According to Market Research, the global machine learning market is poised to reach INR 7632.45 billion by 2027 at a CAGR of 37.12% during the forecast period 2021-2027. The introduction of Machine Learning (ML), which is a subset of AI, has enabled computer systems to learn, identify patterns, and make predictions without being programmed.
• Define Clear Objectives: Start by defining specific business objectives that you want to achieve through ML, considering data availability, complexity, and potential impact on outcomes.
• Data Collection and Preparation: Quality data is crucial for ML projects. Use proper data collection mechanisms and preprocess data for effective model training.
• Create a Test Environment: ML in testing boosts efficiency, reliability, and offers benefits like enhanced accuracy, reduced maintenance, and test data generation.
• Monitor and Maintain Models: ML algorithms can analyze real-time performance data, predict problems in advance, and enable rapid response with minimal user impact.
• Stay Informed about ML Trends: Stay updated on ML trends like IoT, automated ML, no-code ML, MLOps, and cybersecurity to leverage new opportunities and technologies.

2. How are MLOps platforms becoming increasingly vital in accelerating the machine learning initiatives of organizations?

• MLOps has emerged as a transformative discipline at the intersection of ML and software engineering. The MLOps market size is projected to grow from USD 1.1 billion in 2022 to USD 5.9 billion by 2027, at a CAGR of 41.0% during the forecast period. In a world increasingly driven by data and AI-driven insights, MLOps offers a systematic approach to managing the complete lifecycle of machine learning models, from development and training to deployment and ongoing maintenance.
• Enhanced Collaboration: MLOps platforms enable seamless collaboration among data scientists, engineers, and stakeholders. They offer version control for ML models and data, facilitating change tracking and experiment reproducibility.
• Scalability: As ML projects grow, MLOps platforms efficiently scale infrastructure, handling large datasets with distributed computing across clusters or cloud resources, ensuring faster processing and reduced latency.
• Resource Optimization: MLOps platforms optimize resource allocation for ML tasks, leading to cost savings and increased efficiency.
• Automated Model Deployment: MLOps platforms automate ML model deployment, accelerating time-to-market for ML-driven apps.
• Standardization and Best Practices: Using best practices of MLOps and standardizing ML workflows minimizes errors, improves model quality, and enhances productivity.

3. Why is there a need for a guide to effectively manage MLOps that aligns with an enterprise’s objectives and team structure?

• ML has a lot of variability in its behavior, given that models are essentially a black box used to generate some prediction. MLOps requires a more specialized set of tools and practices to address the unique challenges posed by data-driven and computationally intensive ML workflows. A guide to effectively manage MLOps is required for the following reasons:
• To Mitigate Complexity: Businesses are dealing with more data than ever before. Complexity grows with multiple teams like data scientists, data engineers, DevOps, and IT.
• Scalability: Enterprises often require scalable MLOps workflows to handle growth. A guide offers insights on designing such workflows for increased workloads and data volumes.
• Cross-Functional Collaboration: MLOps involves collaboration between various teams, each with its own set of responsibilities and goals. Establishing communication channels and processes to facilitate effective cross-functional collaboration, preventing bottlenecks and conflicts is crucial.
• Standardization: Standardizing MLOps processes and practices across the organization can improve efficiency and reduce the risk of errors. A guide can provide a standardized framework that all teams can follow.

4. How is Pega’s MLOps function differentiated with the use of its own APIs and shadow mode?

• As a data scientist involved in a Pega Customer Decision Hub project, MLOps can help manage the complexity of the machine learning pipeline.
• Pega’s MLOps stands out as it automates the end-to-end process for model import and deployment with the use of Pega’s own APIs, so you can replace an underperforming model with a new one from any source.
• Additionally, with Shadow Mode, you can now compare the performance of your current model vs the candidate using real production data, before deploying the model to production.
• Once you are ready to replace the model, you can now also generate a new change request in 1:1 Operations Manager, to ensure that the update follows standard revision management procedures.
• Additionally, one can create and deploy models directly from your modeling tool to Pega Platform, by using a scripting language of your choice. The API provides you with options to remotely perform MLOps: such as adding a model to the application, reviewing (approve or reject) a model update, and retrieve the status of a model update.