Blog Posts

• What is Enterprise MLOps?

As more enterprises embrace ML across their systems and workflows, interest in MLOps has gained traction in recent years. One of the biggest reasons behind this is the bottlenecks imposed by manual workflows. Lack of automation, coupled with a skewed focus on 'code & complexities' rather than solving the actual business problem, necessitates a clear and well-defined operating model that streamlines development and operations.

Like DevOps, MLOps focuses on --

• Automating by implementing CI/CD pipelines across an ML lifecycle

• Clear and close coordination among data scientists, ML engineers, data engineers, IT leads, business analysts, and all other stakeholders

• Developing tools and best practices for designing, testing, deploying, managing, and monitoring ML solutions.

The endgame of machine learning operations is to define a golden path to well-engineered and easy-to-maintain ML applications.

• Key MLOps Tenets

An ML system is, at its core, a software system augmented by machine learning capabilities. Implementation of DevOps allows efficient and reliable delivery of software systems and MLOps enables the same for machine learning systems, based on the following tenets ---

➤ Scalability

Data scientists and machine learning engineers can scale resources and services up or down as per demand.

➤ Flexibility

Developers can implement any ML framework (PyTorch, TensorFlow, Keras, Theano, Sci-kit learn, etc.).

➤ Automation

MLOps defines three distinct levels of automation, namely, manual process automation, (level 0), ML pipeline automation (level 1), and CI/CD pipeline automation, each executed iteratively as we progress downstream in the MLOps lifecycle.

➤ Reproducibility

Data scientists and engineers can observe & reproduce past experiments with data, model parameters & hyperparameters, etc., by automated versioning of EDA code, training parameters, environments, and infrastructure.

➤ Reusability

Source codes and MLOps pipelines are reusable without running any additional costs and inconsistencies.

➤ Continuous Monitoring

Production data, model performance metrics, and associated business metrics can be monitored & measured conveniently.

➤ Testing

MLOps allows teams to evaluate the quality, quantity, & validity of data, transformations, infrastructure, and regulatory compliance.

he above tenets define the core MLOps principles, which are implemented across three levels of any ML pipeline – data, model, and code.

• The Core Principles of Enterprise MLOps

1. Automation

A primary objective of MLOps is the automated deployment of ML models. Lifecycle workflow steps are automated entirely without the need for any manual intervention. Automated integration and testing help discover problems & bottlenecks quickly & early.

Three levels of automation are seen in standard MLOps practices --

-- Manual Process

This is an experimental & iterative process that is performed during the initial design stages. Data science processes make up the entirety of this stage and involve data preparation & validation, preprocessing, model development, training & testing, error handling, & code testing.

-- ML Pipeline Automation

The next level revolves around automated & continuous training of the model. Whenever new data is available, the model retrains automatically. Data and model performance validation run concurrently.

-- CI/CD Pipeline Automation 

The final level puts in place a CI/CD pipeline for swift and reliable deployment. Continuous Integration/Continuous Development can automate all stages of an MLOps pipeline, from building & training to delivery& operations.

2. Versioning

Versioning tracks ML models and training, testing, & validation data sets. In doing so, teams can --

-- Retrain models with new training data & approaches.

-- Ensure models can self-learn.

-- Track model degradation.

-- Deploy models across different use cases.

-- Roll back to previous versions.

-- Address compliance, ownership, storage, & relevant data-related issues

3. Testing

MLOps defines three scopes of testing, namely,

-- Data & Features Testing

-- Model Development Testing

-- Infrastructure Testing

4. Deployment

As a model is deployed, data features are stored in development & production environments. Containerization of the entire ML stack and the infrastructure & environment variables is done and stored on-premises, on the cloud, or on the edge.

5. Monitoring

Routine monitoring is essential to ensure the model performs as expected. Crucial aspects monitored include --

MLOps defines three scopes of testing, namely,

-- Dependency changes,

-- Model stability,

-- Computational performance,

-- Age & Degradation,

6. Reproducibility

Reproducibility is a vital stage in the MLOps workflow. Every phase of data preparation & processing, model training, testing, and deployment should produce identical results given the same input.

• Conclusion:

Achieving enterprise MLOps ushers in a paradigm shift in how organizations develop, deploy, and manage machine learning solutions. MLOps defines a comprehensive framework for streamlining entire developmental life cycles and cultivates an environment for better collaboration among all the teams involved. It bridges the gap between data scientists, ML engineers, and IT professionals, thereby facilitating methodical development and delivery of machine learning and AI solutions.

Successful enterprise MLOps integration depends on structured & well-defined strategies, the right technological resources, and a working culture that values communication & cooperation.

• How Can ITPN Help?

ITPN has leading-edge capabilities, top-class expertise, and pioneering expertise in tailoring business-specific MLOps strategies. We have top-tier domain experts who will optimize your business’s machine learning development and operations and boost productivity and efficiency. Please contact us with your queries or for any assistance regarding our services.