Machine learning operations

Optimizing machine learning operations for scalable enterprise AI

Seamless data-driven decision delivery for enterprises

Challenges slowing down MLOps success

Multiple friction points hinder the seamless AI scalability and integration

Data scientists rely heavily on IT teams for operationalization, creating bottlenecks

Ensuring models stay accurate and relevant over time remains a challenge

Models work in the lab but struggle in real-world deployment

Scalability and infrastructure complexity, slows things down

Long release timelines delay AI-driven business impact

Why enterprises need MLOps for scalable and efficient AI

MLOps keeps your AI scalable, efficient, and ready for real-world impact

MLOps: Streamlining AI from data to deployment

Continuous integration

Tests and validates code and components

Adds testing for data quality and integrity

Ensures models are validated before deployment

Continuous integration

Tests and validates code and components

Adds testing for data quality and integrity

Ensures models are validated before deployment

Continuous delivery

Automates deployment of trained models

Focuses on delivering a seamless ML training pipeline

Ensures a smooth transition to a live prediction service

Continuous delivery

Automates deployment of trained models

Focuses on delivering a seamless ML training pipeline

Ensures a smooth transition to a live prediction service

Continuous training

Designed specifically for ML systems

Automates model retraining based on new data

Enables seamless re-deployment of updated models

Continuous training

Designed specifically for ML systems

Automates model retraining based on new data

Enables seamless re-deployment of updated models

Continuous monitoring

Automates retraining as data evolves

Unique to ML systems and their lifecycle

Streamlines model re-deployment for continuous improvement

Continuous monitoring

Automates retraining as data evolves

Unique to ML systems and their lifecycle

Streamlines model re-deployment for continuous improvement

Fractal’s MLOps solutions: Powering scalable and efficient AI

Fractal delivers AI automation across all major clouds with three engagement models

Full project

Building MVP

Staff augmentation

Democratize model and artifacts

Effectively audit and monitor your models

Accelerate time-to-value and time-to-deployment

Decrease dependency on IT for model deployment

Increase model scalability during training and serving

Efficiently manage data error and model performance

Simplify model management, governance, deployment, and monitoring

Thought leadership

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Data drift: Identifying. Preventing. Automating

Disruption has given a new dimension to the data drift challenge. Businesses grappled with changing consumer behaviors, leading to changing data patterns

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Satellite image analytics — IVA Geo

Geospatial analysis leverages geographic data for decision-making

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From Inspection to Automation

The role of machine vision in modern manufacturing

Our experts

Snehotosh Banerjee

Lead ArchitectAI@Scale, Machine Vision and Conv. AI

Suraj Amonkar

Chief AI Research Officer

Unlock the power of vision AI

Recognition and achievements

Select Fractal accolades

Leader

The Forrester Wave: Customer Analytics Services Q2, 2025

Representative vendor

Gartner Hype Cycle for Consumer Goods, 2026

Great Place to Work

Great Place to Work® across four regions: India (9th year), USA (5th year), UK (5th year) and UAE (2nd year)

Recognition and achievements

Select Fractal accolades

Leader

The Forrester Wave: Customer Analytics Services Q2, 2025

Representative vendor

Gartner Hype Cycle for Consumer Goods, 2026

Great Place to Work

Great Place to Work® across four regions: India (9th year), USA (5th year), UK (5th year) and UAE (2nd year)

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WB Policy

Supplier Code of Conduct

Global Code of Conduct

Human Rights Statement

Modern Slavery Statement

CSR Policy

ABAC Policy