Serverless AI Layer
Shrink the time to deploy each ML Model from months to minutes.
Most teams are running into a wall when it comes to deploying their ML models.
The Serverless AI Layer is your opportunity to gain a lead in your machine learning efforts.
Automate DevOps for Your Machine Learning and Deep Learning Models
Deploying ML models manually is a common pitfall that most teams find out the hard way. It creates more work for high value team members, slows down shipping, and is guaranteed to become a patchwork mess. The AI Layer, on the other hand, automates, optimizes, and accelerates every step in model deployment and management.
Free up your team to focus on higher value opportunities
If you try to add deploying, managing, and optimizing ML models to your current team's workload your progress will slow to a crawl. The stack for deploying ML models is completely different from the rest of your software. The Serverless AI Layer lets your data scientists focus on data science, not DevOps.
Utilize powerful hardware efficiently
The Serverless Artificial Intelligence Layer uses both GPUs and CPUs, utilizes advanced hardware management to maximize performance and optimize cost, and scales up and down to match your needs.
As someone that has spent years designing and deploying machine learning systems, I'm impressed by Algorithmia's serverless microservice architecture – it's a great solution for organizations that want to deploy AI at any scale.
Data Scientists Love it Because...
Use the language(s) you want
Are some of your models in R and others in Python? No problem. The AI Layer can run models, functions, and algorithms in most popular languages.See all the languages we support
Supercharge your applications with pre-trained ML models
The AI Marketplace has over 5,000 pre-trained ML models, algorithms, and functions available for you to incorporate into your data pipelines.
Save Time by Pipelining
The data scientists on your team are often re-writing code to collect, clean, and prep data—our serverless infrastructure makes pipelining simple and easy.
When you work with Algorithmia, you have a direct line of contact and support from engineers and data scientists who ensure that your AI/ML model deployment is successful.
No more DevOps needed for Data Scientists
You wouldn’t ask your graphic designers to merge pull requests—so why should your data scientists worry about DevOps and model deployment? Let them focus on what they do best: keeping up with the rapidly progressing field of data science and building awesome models.
DevOps Loves it Because...
Massively Parallel Computing
The AI Layer will run each of your models in parallel and allows you to pipeline the results together. Huge jobs can be performed quickly.
GPUs and CPUs optimized for ML
Scheduling and utilizing the full power of your hardware is a huge challenge. Our advanced scheduler allows us to offer GPUs at the same low cost as CPUs.
Only pay for what you use
Our serverless infrastructure means that you only pay by the second when you’re actively using the models.
Serverless architecture scales to your needs
Compute for ML models is extraordinarily spikey. The Serverless AI Layer will scale up and down by the second. You get the performance you need with none of the work or cost of managing the hardware.
Engineers Love it Because...
From pre-trained model to fully-scalable deployment in minutes
It’s simple. Git push your pre-trained model, algorithm, or function in the language of your choice—and a few seconds later you have an API endpoint ready for any scale.
Your ML Models are containerized, wrapped in an API, and ready for scale
The amount of development time that goes into deploying ML models at scale is restrictive. That’s why big tech companies like Uber and Google have built their version of the AI Layer; Algorithmia makes these powerful tools available to everyone.
Automatically versions your models
Behind the scenes the AI Layer is making sure that your legacy models can continue to run without breaking any endpoints. This allows for easy testing between models.