Optimizing machine learning operations for scalable enterprise AI
Optimizing machine learning operations for scalable enterprise AI
Seamless data-driven decision delivery for enterprises
Seamless data-driven decision delivery for enterprises
Challenges slowing down MLOps success
Multiple friction points hinder the seamless AI scalability and integration
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Data scientists rely heavily on IT teams for operationalization, creating bottlenecks
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Data scientists rely heavily on IT teams for operationalization, creating bottlenecks
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Data scientists rely heavily on IT teams for operationalization, creating bottlenecks
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Ensuring models stay accurate and relevant over time remains a challenge
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Ensuring models stay accurate and relevant over time remains a challenge
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Ensuring models stay accurate and relevant over time remains a challenge
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Models work in the lab but struggle in real-world deployment
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Models work in the lab but struggle in real-world deployment
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Models work in the lab but struggle in real-world deployment
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Scalability and infrastructure complexity, slows things down
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Scalability and infrastructure complexity, slows things down
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Scalability and infrastructure complexity, slows things down
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Long release timelines delay AI-driven business impact
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Long release timelines delay AI-driven business impact
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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
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Continuous integration
Tests and validates code and components
Adds testing for data quality and integrity
Ensures models are validated before deployment
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Continuous integration
Tests and validates code and components
Adds testing for data quality and integrity
Ensures models are validated before deployment
1
Continuous integration
Tests and validates code and components
Adds testing for data quality and integrity
Ensures models are validated before deployment
2
Continuous delivery
Automates deployment of trained models
Focuses on delivering a seamless ML training pipeline
Ensures a smooth transition to a live prediction service
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Continuous delivery
Automates deployment of trained models
Focuses on delivering a seamless ML training pipeline
Ensures a smooth transition to a live prediction service
2
Continuous delivery
Automates deployment of trained models
Focuses on delivering a seamless ML training pipeline
Ensures a smooth transition to a live prediction service
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Continuous training
Designed specifically for ML systems
Automates model retraining based on new data
Enables seamless re-deployment of updated models
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Continuous training
Designed specifically for ML systems
Automates model retraining based on new data
Enables seamless re-deployment of updated models
3
Continuous training
Designed specifically for ML systems
Automates model retraining based on new data
Enables seamless re-deployment of updated models
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Continuous monitoring
Automates retraining as data evolves
Unique to ML systems and their lifecycle
Streamlines model re-deployment for continuous improvement
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Continuous monitoring
Automates retraining as data evolves
Unique to ML systems and their lifecycle
Streamlines model re-deployment for continuous improvement
4
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’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
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Democratize model and artifacts
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Democratize model and artifacts
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Democratize model and artifacts
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Effectively audit and monitor your models
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Effectively audit and monitor your models
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Effectively audit and monitor your models
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Accelerate time-to-value and time-to-deployment
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Accelerate time-to-value and time-to-deployment
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Accelerate time-to-value and time-to-deployment
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Decrease dependency on IT for model deployment
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Decrease dependency on IT for model deployment
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Decrease dependency on IT for model deployment
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Increase model scalability during training and serving
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Increase model scalability during training and serving
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Increase model scalability during training and serving
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Efficiently manage data error and model performance
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Efficiently manage data error and model performance
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Efficiently manage data error and model performance
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Efficiently manage data error and model performance
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Efficiently manage data error and model performance
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Efficiently manage data error and model performance
Thought leadership
Our experts
Snehotosh Banerjee
Lead ArchitectAI@Scale, Machine Vision and Conv. AI
Snehotosh Banerjee
Lead ArchitectAI@Scale, Machine Vision and Conv. AI
Snehotosh Banerjee
Lead ArchitectAI@Scale, Machine Vision and Conv. AI
Suraj Amonkar
Chief AI Research & Platforms Officer
Suraj Amonkar
Chief AI Research & Platforms Officer
Suraj Amonkar
Chief AI Research & Platforms Officer
Unlock the power of vision AI
Unlock the power of vision AI
Connect with our experts today
Connect with our experts today
Connect with our experts today
Recognition and achievements
Named leader
Customer analytics service provider Q2 2023
Representative vendor
Customer analytics service provider Q1 2021
Great Place to Work
8th year running. Certifications received for India, USA,Canada, Australia, and the UK.
Recognition and achievements
Named leader
Customer analytics service provider Q2 2023
Representative vendor
Customer analytics service provider Q1 2021
Great Place to Work
8th year running. Certifications received for India, USA,Canada, Australia, and the UK.
Recognition and achievements
Named leader
Customer analytics service provider Q2 2023
Representative vendor
Customer analytics service provider Q1 2021
Great Place to Work
8th year running. Certifications received for India, USA,Canada, Australia, and the UK.
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