2 minutes read
October 27, 2023
Technical Overview of Pipeline Core: Part 1/3
Explore the first segment of our deep dive into Mystic AI's Pipeline Core, an enterprise grade ML deployment platform. Discover how its user-friendly setup, smart routing, auto-scaling, and a suite of other features make it a go-to solution for Founders, Data Scientists, Developers, and ML practitioners. Stay tuned for the next part, where we'll unveil its cost-effective pricing model.
In the world of Machine Learning (ML) and Artificial Intelligence (AI), deploying a model to production is a pivotal phase. Mystic AI's Pipeline Core emerges as a simple solution for founders, developers, data scientists and ML/AI engineers seeking to take their python code to production. Our platform doesn't just facilitate seamless deployment but also stands as a testament to cost-efficiency, cutting down cloud expenses by nearly 88%, all while ensuring ultrafast inference speeds with sub-25ms latency.
Let’s explore the attributes that make Pipeline Core an indispensable tool for ML practitioners, AI startups and large enterprises:
Easy to get started:
Deploy your AI pipelines with Pipeline Core with extremely minimal hassle. Here’s the straightforward workflow:
Authenticate: Register your cloud or on-prem credentials with the Pipeline Core Command Line Interface (CLI).
Initialize: Trigger a cluster creation via the CLI and in just 15 minutes, your setup is ready
Deploy: Draft a python file, encapsulate your code with the python SDK, and upload it.
Execute: A simple API request and there you go! Your code is up and running on Pipeline Core.
Having set the stage with an easy kickstart, Pipeline Core doesn’t stop at just simplifying the deployment. It’s engineered to optimize the performance of your ML models once they are up and running. The subsequent features focus on enhancing execution efficiency, handling traffic surges, and much more.
Pipeline Core doesn’t just execute; it executes smartly. By gauging a range of metrics, it selects the fastest route to run your code, dodging cold starts and managing queues adeptly.
Traffic spikes are no match for Pipeline Core’s auto-scaling feature. It scales resources in tandem with traffic, ensuring smooth operation while keeping a tight lid on costs.
With Pipeline Core, GPU sharing is redefined. It enables multiple models to operate on a single GPU, optimizing utilization.
Future-Proof Your Deployments:
Pipeline Core isn’t just about the now, it’s about the future. It backs CI/CD integrations for live model redeployment, model versioning to monitor enhancements, and AB testing for real-time model performance validation.
Cloud Aggregation for Cost Efficiency:
Cost-efficiency is at the core of Pipeline Core, allowing the pooling of GPUs from varied providers into one central deployment.
For those inclined towards in-house resources, Pipeline Core integrates smoothly with on-premise resources, ensuring your local deployments are on par.
Monitoring and Alerting:
With Pipeline Core’s monitoring dashboard, stay updated on traffic, error rates, and resource utilization. And if things go awry, its alert system keeps you informed via Slack, Microsoft Teams, or email.
In the next segment (2/3), we’ll delve into how the competitive pricing of Pipeline Core makes it a cost-effective choice without compromising on quality and performance.