Product / CT-Passport
CT-Passport
Your Passport to Consolidate Legacy AI Platforms on a Unified Framework
CT-Passport
Your Passport to Consolidate Legacy AI Platforms on a Unified Framework
Product / CT-Passport
CT-Passport is a comprehensive, GenAI-powered framework designed to unify large-scale AI/ML operations. Migrate seamlessly from fragmented, outdated systems to a modern, efficient platform powered by Databricks.
CT Passport + Databricks:
A Unified AI Experience
Revolutionize your AI strategy with the combined power of CT Passport and Databricks
Disparate AI Platforms: Key Challenges
Manual Efforts & Limited Scalability
Manual Efforts & Limited Scalability
Isolated systems lead to inefficient workflows, wasting resources, and constraining growth.
Isolated systems lead to inefficient workflows, wasting resources, and constraining growth.
Data Silos & Redundancy
Data Silos & Redundancy
Data movement across multiple platforms leads to data duplication and complicated workflows.
Data movement across multiple platforms leads to data duplication and complicated workflows.
Code Conversion
Code Conversion
Migrating codebases across platforms requires significant effort and introduces potential risks and errors.
Migrating codebases across platforms requires significant effort and introduces potential risks and errors.
Core Functionalities of CT Passport
Advanced LLM Observability Platform
Monitor latency, trace operations, analyze retrieval patterns, and ensure responsible AI practices with a robust observability platform.
Lakehouse Monitoring
provides proactive anomaly detection, unified data and ML model visibility, and automated root cause analysis to ensure high-quality, reliable AI assets.
Integrated Model Management
Track critical metrics, visualize trends, detect model drift, and manage the entire model lifecycle with versioning, registration, governance, and experimental tracking.
IaaC-Driven Automation Framework
Employ metadata-driven asset bundles, skeletons, model templates, and CI/CD workflows for end-to-end automation with an MLOps-focused stack.
Prompt Management
Empowers centralized orchestration, version control, and optimization of prompts, enhancing AI-driven workflows.
Experiment Tracking
Facilitate monitoring of AI/ML experiments, capturing all metadata, metrics, and outcomes for reproducibility and evaluation.
Governance
Establish a robust framework for managing data integrity, AI/ML lifecycle management, and platform compliance.
GenAI-Powered Accelerator
Enable efficient migration of model scripts across platforms with advanced code comprehension, conversion, validation, and testing for run-ready models.
Our Strategic AIOps Migration Methodology
Powered by Celebal Tech's expertise and GenAI-driven script conversion, CT-Passport streamlines AI platform consolidation through a structured and well-defined approach. CT-Passport utilizes the most advanced techniques and expertise to migrate and modernize client’s overall AI platform and all associated workloads.
Delivering Measurable Business Outcomes
7X
Faster processing and inference, enabling timely insights and improved operational workflows.
40%-60%
Accelerated production timelines for AI models, driving faster deployment and operational agility.
24*7
Ensure uninterrupted availability and reliability of AI systems with robust infrastructure.
Success Stories
Celebal Technologies implemented an MLOps solution for a US-based ITES leader to optimize ML application budgets, enhance data accuracy, and improve campaign management. Using CT Passport, the solution featured Kedro-powered pipelines, GitHub integration, and advanced models like Neural Networks and XGBoost. This resulted in a 75% increase in operational efficiency, 500+ optimized models, 10+ automated pipelines, and a fully scalable, sustainable framework.
A leading client in the semiconductor and sensors industry faced challenges with fragmented data pipelines, inefficient ML workflows, and limited parallelization. Celebal Technologies optimized the system by integrating a unified data and model pipeline, utilizing distributed Spark for real-time processing, and implementing custom layers for task-specific operations. Automated training, inference, and retraining pipelines were established. The solution achieved a 70% reduction in data extraction time, 82% in transformation time, 68% in preprocessing and training time, and a 75% decrease in manual efforts.
Take the first step toward unified AI operations
Experience seamless AI integration and unlock operational efficiency with a free demo.
