Benefits
Scalability and reliability with a robust architecture
MLOps framework provides robust architecture and tools for scaling machine learning models in production.
Seamless collaboration and compliance
MLOps framework streamlines the entire machine learning lifecycle from model development to deployment, enabling teams to collaborate efficiently while meeting security and regulatory requirements.
Accelerated Time-to-Market
MLOps automates development and deployment, cutting down delays and delivering models to production faster.
Mastering the key challenges
MLOps addresses key challenges in AI by providing the necessary architecture to ensure scalability and reliability, critical requirements for deploying models in production. It bridges the often difficult gap between development and deployment, overcoming obstacles related to model performance, scalability, and consistent delivery in real-world applications.
GFT's MLOps principles
Streamlined data extraction, loading and transformation
MLOps framework ensures efficient and streamlined data operations, from extraction and transformation to loading and storage.
Modular and parametrisable models
MLOps framework allows for easy modularisation and parametrisation of trained models, enabling rapid adjustments and fine-tuning.
Real-time monitoring and adaptive learning
We implement continuous monitoring and adaptive learning mechanisms, providing alerts and enabling a correct administration and maintenance of the system.
Accelerate with MLOps framework
Enable proactive adjustments to optimise factory efficieny.
Anticipate production environment characteristics and changes, preventing quality handicaps and avoidong defects in the final result.
Optimise cost critical asstes like HVAC systems or blast furnaces, achieving significant saving in energy consumption.
Forecast conditions affecting machinery an enable proactive maintencance scheduling, reducing unplanned downtime.
