Handit.AI Documentation
Last updated
Last updated
AI model deployment and management often fall short of delivering the expected business outcomes, with up to 80% of AI projects never scaling beyond pilot stages. To address this challenge, Handit.AI provides a comprehensive AI Lifecycle Management Platform that ensures AI models are continuously aligned with business goals and KPIs.
Handit.AI includes a robust tracking system that enables businesses to effectively monitor and optimize AI models in production. With seamless integration into your existing workflows, the platform automates the tracking of AI inputs and outputs, ensuring that models are continuously optimized and their performance remains aligned with key business objectives.
Continuous Monitoring of AI Models Detect performance degradation and data drift in real-time, ensuring your models remain accurate and reliable.
Automated Data Capture Handit.AI intercepts HTTP requests (via Axios and Fetch) to automatically track model inputs and outputs, streamlining the tracking process without requiring manual intervention.
Seamless Integration The platform easily integrates into your existing AI infrastructure, supporting popular tools such as Amazon SageMaker and TensorFlow, reducing complexity and accelerating time to value.
Handit.AI is designed to solve the fundamental issues AI projects face—disconnection between models and business goals, performance degradation, and lack of continuous optimization. By integrating our tracking tools, your AI models become transparent and measurable, allowing you to fine-tune them to your specific business needs and drive long-term success.
Key benefits include:
Improved AI ROI: Directly connect model performance to business outcomes, ensuring that your AI initiatives deliver the value they promise.
Proactive Issue Prevention: Identify performance issues and data drift before they escalate, allowing for rapid optimization.
Reduced Time to Market: Automate much of the manual work required to monitor and optimize AI models, accelerating the deployment process.
