Organize, Manage, and Deploy your company’s ML model portfolio

Most companies are investing in Machine Learning, yet very few are currently deploying Machine Learning models throughout their organizations. The Enterprise AI Layer sets the gold standard for companies who are serious about their AI/ML investments.

Automate DevOps for Deploying ML 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 automates, optimizes, and accelerates every step in model deployment and management.

1. You train your models with the framework of your choice.
2. A simple git push into the AI Layer makes your model ready for scale.
3. The AI Layer manages the hardware and makes the model available as an API.

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 front-page of Reddit 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 biggest tech companies like Uber and Google have built their version of the AI Layer; Algorithmia makes these powerful tools available to everyone.

Manage your ML model portfolio

The AI Layer organizes your entire library of AI/ML Models, functions, and algorithms—making them discoverable based on your own permissions. Dashboards and logs help you understand how models are being used across your organization, and help you plan and allocate resources.

Managed Kubernetes

Your hardware is being optimized behind the scenes—it’s going to be fast, scalable, and ready for any challenge your team takes on.

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.

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.

VP of Engineering, Artificial Intelligence at Google

Free up your Data Scientists to be more efficient

Data Scientists can deploy the models that they’ve already built, in the language of their choice, in just a couple of minutes. The Enterprise AI Layer removes coordination costs, increases model iteration speed, and allows data scientists to continuously ship and improve models. No more waiting months on engineering teams to deploy the models you’ve already worked so hard on.

Make your team more efficient

Deploying one AI/ML Model

Data Engineers Data Scientists Dev Ops Deployment Time
Manual Deployment 2 3 2 In Months
With Algorithmia's AI Layer 1 1 0 In Minutes

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. Stay competitive by making your applications AI-capable.

White-glove Support

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 be worrying 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.

Algorithmia empowers U.S. Government agencies to rapidly deploy new capabilities to the AI layer. The platform delivers security, scalability and discoverability so data scientists can focus on problem solving.

Partner at In-Q-Tel

Push hardware to the limits on performance and efficiency

For the first time ever, you can leverage powerful GPUs working in parallel while only paying for the seconds you’re actively running models. Our engineers have spent years building the ideal ML infrastructure—squeezing out maximum performance at minimum cost.

Your model usage is optimized, monitored, versioned, and secure.

Massively Parallel Computing

The AI Layer will run each of your models in parallel and automatically pipelines 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.

Pipelining will save you so much time

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.

Elastic Scaling is key

Compute for ML models is extraordinarily spikey. The Serverless Artificial Intelligence 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.

AI Layer Architecture

Today most AI/ML models are still being deployed manually, which requires a lot of time, coordination, and engineering resources. We're working with Algorithmia to help companies deploy, iterate, and scale faster on Azure with the Enterprise AI Layer.

Senior Program Manager at Microsoft

We're here to help

Our team of ML infrastructure specialists are here to ensure you have a plan to deploy your ML investments. Schedule an initial meeting with our team today.