KenAI – Accelerate your MLOps journey
AI is being embedded across products and processes by organizations across the globe, irrespective of the revenue size, domain, and technology prowess. The result is that organizations are experimenting with building a multitude of models to solve new-age complex problems with a plethora of different use cases.
The point of contention comes while moving the needle from experimentation to scaling AI, most fail as they take months to deploy and then monitor the models. The outcome is a lack of scale, inability to manage the operations side, and difficulty in governing the outcomes of models. This leads to most AI projects being shelved due to a lack of business value and the risks that it poses to business. LTIMindtree KenAI addresses the core challenges of scaling, managing, and governing the models on cloud data platforms. It provides a complete playbook to standardize, streamline and accelerate your AI journey using inbuilt tools and utilities.
KenAI – Key Building Blocks
LTIMindtree KenAI provides a powerful automation and lifecycle management to simplify and accelerate operationalizing and managing AI models. It helps deliver simplified machine-learning operations leveraging automation, predictability, and responsible AI.
KenAI Automate
Automated pipelines for operationalizing the models along with reusable components to help fastrack the model deployment process in other languages and frameworks
- Automate Pipeline: End to End-model lifecycle management for training, inference, and drift management
- Reusable Components: Reusable components to help with integrated model operations
- Infra as a Code: Code-based automated provision of infrastructure for compute and scoring
- Model Deployment: Standardize and simplify model deployment process
KenAI Assure
AI/ML model quality assurance with in-built test cases to help quantify the performance of the models before deploying them on the cloud data platform
- Data Quality: Pre-defined tests to check data quality and biasness in data
- Model Quality Assurance: Provide predictability for model performance with in-built test cases covering train-test model and deployed models
- Model Sustainability Check: Ensure models are sustainable and energy efficient
- MRO Dashboard: Model risk officer dashboard covering an overview of model health performance across key KPIs
KenAI Govern
Comprehensive model explanations & governance for serialized machine learning models providing key insights into the predictions to drive transparency and adoption
- What-If Analysis: Analyse the impact of features and their corresponding values in the model insights
- Model Explainability: Decipher the model with ease and help understand how the model produces the output
- Model Auditability: Ability to perform lineage to help decipher the models at ease
- Responsible AI: Ensure adherence of AI principles to ensure regulatory compliance and adherence to fair AI
KenAI Monitor
Unified AI/ML model monitoring to provide a centralized view of all the models deployed across the organization along with monitoring KPIs to ensure continuous monitoring of models
- Data Drift Management: Data comparison across multiple versions and features of models
- Model Drift Management: Evaluate the model performance over a pre-defined period of time
- Model Service Health: Service health parameters to showcase key infrastructure usage
- Continuous Monitoring: Ensures continuous monitoring by providing automated alerts along with integrated re-training mechanism
Key Benefits of KenAI
Automation:
Accelerate the model deployment with 2x faster time to market leveraging automated workflows
Predictability:
Maintain the accuracy of predictions after models are deployed with both business and operational monitoring
Responsible AI:
Safeguard from business and reputational risk with enhanced governance, security, and adherence of AI principles
Simplified MLOps:
Ensure frictionless collaboration with simplified end-to-end machine learning operations to deliver CI/CD/CT/CM
Responsible AI: An Integral part of ModelOps
Responsible Artificial Intelligence (AI) has become integral to AI/Machine Learning (ML). As organizations experiment with new-age use cases around them, the need is to have a h...
Integrated MLOps at Scale
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Centralized Model Monitoring
Keep centralized track of all your AI/ML models leveraging kenAI