MLOps – A Key Lever In Revolutionizing AI/ML Adoption For Industries

Overview

As the world slowly but surely emerges out of the Global Pandemic situation, the role of Artificial Intelligence and Machine Learning in driving digital business transformation has greatly increased. The impetus to move to the cloud and the growing number of ML models which saw phenomenal growth during the Pandemic, is expected to sustain over a period going forward. However, while operationalizing Artificial Intelligence, it has been found that merely 27% of the projects piloted by organizations successfully move to production. The anomaly between what AI/ML can achieve and what it is currently capable of, arises out of several challenges – most of them are related to model development, iteration, deployment and monitoring. These issues need to be addressed if AI/ML has to revolutionize the global business landscape. Organizations that have already started their journey to operationalize AI/ML or are developing Proofs of Concept (PoC) can pre-empt some of the challenges by proactively integrating best-practices in MLOps to ensure the smooth development of models and address issues of scalability. This compendium compiled by NASSCOM in association with Genpact and EY encapsulates the best practices to guide organizations on effective set up, management, and scaling of ML operations.

Available in Soft Copy Only

Key Findings

What is MLOps?

MLOps is a set of practices and methodologies that help in automating ML model development, achieve automated and reliable ML model deployment, consistent model training, model monitoring, rapid experimentation, reproducible models, and accelerated models
It typically starts with Data Engineering, followed by Algorithm Development, Model Deployment, Model Monitoring and Model Hypercare
It has four main pillars – Model Lifecycle Management, Model Versioning & Variation, Model Monitoring & Management and Model Governance

Need for MLOps?

Just as DevOps came in around 2007-2008 to make SDLC more streamlined and compliant, MLOps holds the promise of ensuring smooth deployment of ML solutions
MLOps combines the best of automation, IT operations and management and Continuous Development & Continuous Integration (CI/CD) in Machine Learning and Artificial Intelligence
Helps to industrialize ML Models and thrive towards a DevOps culture
Addresses key challenges around data quality, model decay, and data locality
Helps to pivot from merely developing Data Science capabilities towards moving the model to production
The rise of AutoML tools and platforms have started to democratize data mining and decision sciences

What are the benefits of MLOps?

Reduced time-to-market for ML products
Improved RoI on AI/ML initiatives
Advanced Data Management
Speedier innovation with ML-driven products
Improved transparency and model governance

Dimensions of MLOps – 6 Key Pillars The compendium brings out 6 key pillars of MLOps best-practices divided in to two broad categories. Each of these pillars have been discussed in detail in the handbook

Implementation Pillar which includes Data, Training Model and Deployment
Business & Operations Pillar which includes Innovation and Future, Control & Governance, and Investment & Change Management

A four-stage MLOps implementation framework has been proposed with automated ML platforms, data pipelines, continuous model monitoring, automated deployment, and reorganized team structures

Define & Design – defining project charter, understanding the business problem and the context, design architecture, and data pipeline design
Data pre-processing – Collecting data sources, configuring data ingestion, data transformation and creating data storage
Model Development – Model training and model validation, model testing and model packaging
Model Deployment & Monitoring – The final stage of an ML project involves three steps; model serving, model performance monitoring and model performance logging

ML Model Management and Model Testing are critical components while operationalizing MLOps

The Future MLOps promises standardization of processes and methodologies and is expected to boost efficiencies in terms of cost, quality, and time to value. Regardless of the industry or use-case, MLOps processes are the common thread that enables data te