MLOps: Your Copilot for AI - Can Your Models Land on Water?

January 15, 2009: US Airways Flight 1549 takes off from LaGuardia Airport. Minutes later, both engines fail after a bird strike. In those crucial moments, Captain Chesley “Sully” Sullenberger makes a decision that will go down in history - to land on the Hudson River , saving all onboard.

Now, imagine your AI models are that plane. They've been developed, trained, and deployed, but unexpected challenges arise in the production environment. Who's your "Sully" in this scenario?

Just as Sully's years of experience and cool-headed decision-making saved the day, we look to MLOps to provide the expertise and systems to navigate the complexities of machine learning in production.

Born from the fusion of machine learning and DevOps, MLOps addresses several critical challenges in AI implementation. It streamlines the model development process, ensures consistent model performance in production, manages model versioning and reproducibility, automates model monitoring and maintenance, and bridges the gap between data science and IT operations.

It's the backbone that ensures our AI flights take off, operate smoothly, and land safely, even if that landing spot is the Hudson River of real-world data.

In this issue of AI of the Tiger, we explore how MLOps provides companies with new tools to streamline AI operations, enhance model performance, and execute AI strategies more precisely.

Fasten your seatbelts - we're about to take off into the world of MLOps!

From Manual Checks to Automated Efficiency: P&C Insurer's AI Models Soar with 70% Boost

In the complex world of insurance, a Fortune 500 P&C Insurer with over 200 AI models deployed across various functions, needed help managing model monitoring and retraining efficiently. They needed a centralized control tower for model information and proactive issue identification. Our team at Tiger implemented a comprehensive Model Management Framework, enabling real-time monitoring and smoother operations. The result? A 70% reduction in manual efforts and model training time cut from 4-5 weeks to just 2-3 weeks. Learn how…

Energy Industrial Firm's ML Control Tower Accelerates Development by 40%

Different folks can lead to too many different strokes. A fragmented ML development process doesn’t always spell smooth sailing. Here’s where MLOps - can help. Our client, a large energy industrial investment firm had that same issue. While different teams were using varied tools and approaches, leading to inconsistencies, duplicated efforts, and long development cycles. To address these challenges, a centralized Self-Service ML platform was implemented. This platform provided a unified environment for data preparation, model development, and deployment. It lead to a 40% reduction in time to production, allowing their AI models to take off faster

CPG Giant's Computer Vision Platform Lands 60% Reduction in Manual Efforts

A leading US-based CPG company faced challenges managing multiple Computer Vision use cases across teams. Each project required substantial time and resources for setup and deployment, creating bottlenecks. The client needed a centralized platform to streamline their Computer Vision operations and accelerate AI-driven initiatives. Our team developed an end-to-end Computer Vision platform that automated their operations. Manual efforts dropped by 60%, and onboarding new use cases became as smooth as a well-executed landing. Read more...

Tiger MLCore: Bridging Data Science and Business for Seamless AI Operations

As enterprises scale their AI initiatives, maintaining continuous control over ML models becomes crucial. Traditional monitoring approaches often fall short, lacking business context and seamless integration with MLOps processes. Enter Tiger MLCore: an advanced ML observability and model management platform designed to address these challenges head-on.?It provides real-time insights into model performance, automates monitoring tasks, and helps maintain the health of ML portfolio.

Learn how Tiger MLCore can improve your ML operations and drive business value.

As the AI skies become increasingly crowded, are your organization's MLOps ready to handle the traffic? Or are you still relying on manual control towers? We'd love to hear your thoughts. How is your organization navigating the complex airspace of AI?