Solving the “Cloud Challenge” for Enterprise AI

The tech industry is In the midst of a significant transformation, marked by the rise of Enterprise AI—a precursor to a groundbreaking era of automation.

Artificial Intelligence (AI) is a catalyst for business growth, offering multifaceted benefits:

1. Enhanced efficiency: Automation through AI streamlines processes and minimizes manual efforts.
2. Accelerated time to production: AI ensures swift and consistent service delivery.
3. Innovation discovery: AI uncovers opportunities for novel products and services.
4. Informed decision-making: AI-driven insights empower data-informed strategic choices.

AI's versatility makes it applicable across industries, permeating numerous business operations—from marketing and customer service to supply chain management. However, integrating AI/ML within organizations poses intricate challenges, especially during deployment.

Creating AI is complex enough – why add unnecessary complications and costs during deployment?

In the world of Enterprise AI, companies encounter several challenges as they venture into creating and deploying machine learning models. The journey begins with defining a clear business use case for the AI project, followed by data collection and preparation, model training, ongoing refinement, testing, and finally, deployment.

Each step presents its own set of hurdles, but it’s the deployment phase where many organizations hit a wall. Cloud expenses skyrocket, and the intricacies of managing deployment, particularly with an MLOps team dedicated solely to optimizing the serving and monitoring of deployed models, become a significant overhead.

Challenges in Managing Enterprise AI/ML when deploying in the cloud 

The process of deploying ML models often necessitates a specialized MLOps team to manage the serving and monitoring of these models—a time-consuming and costly affair. The need for such specialized oversight can deter companies from fully embracing AI, despite its numerous advantages.

This is where we, at Mystic AI, step in to help.

Mystic AI: Pipeline Core

Mystic AI’s platform, Pipeline Core, offers a comprehensive  solution to these hurdles. Companies can easily and reliably deploy ML models on their existing infrastructure without requiring a team of MLOps engineers. Through our Python SDK, data scientists immediately obtain an endpoint from their model, or any open-source models, speeding up the deployment process.

Once uploaded, our platform handles routing, multi-cloud scaling, caching, GPU optimization, and other features to provide the ultimate ML inference platform. This not only simplifies the deployment process but also significantly reduces the overhead of maintaining a specialized team.

Advantages of Mystic AI: Pipeline Core

Here are a few advantages of leveraging Pipeline Core for your enterprise: Easy deployment: No need for a separate environment.

No MLOps team required: Reducing overhead.

Instant endpoints: Speeding up time-to-market.

Reduced Cloud bills: Serverless infrastructure optimizes the GPU’s

Explore Pipeline Core to learn more about how Mystic AI could help your enterprise leverage the transformative power of AI.

Conclusion

Dive into your AI journey with a trusted partner who has been working with companies to deploy AI since 2019. Mystic are a highly capable, dedicated team; working 24/7 to deliver incredible results at highly competitive rates.

Learn more about Pipeline Core | Explore our Pay-as-you-go pricing