Data scientists never have to worry about infrastructure again
75% of data scientists' time is spent on infrastructure tasks. [1]
The AI Layer extends your team's potential by automating DevOps
for ML and allowing rapid iteration of your model portfolio.
Data Scientists
Current Challenges
- New frameworks are rapidly released
- Infrastructure tasks consume valuable time
- Hard to scale models into production
AI Layer Solution
Deploy, evaluate, and iterate models at your own pace.
DevOps & Security
Current Challenges
- ML expertise is uncommon
- New infrastructure requirements
- Need to serve at enterprise scale
AI Layer Solution
Monitor, govern, and manage the company-wide ML model
portfolio.
ML Leadership
Current Challenges
- ML is top priority, clear strategy needed
- Insight into ML progress is critical
- Tech stack must be capable of enterprise scale
AI Layer Solution
Ensure ML projects reach maturity with best-in-class
security and auditability.
What is the AI Layer?
The Artificial Intelligence Layer automates your data science
and machine learning workflows and allows you to deploy and
manage models at scale.
CONNECT
Connect major data sources, orchestration engines, or step
functions.
DEPLOY
Deploy models from major frameworks, languages, platforms, or
tools.
SCALE
Scale model inference on infrastructure with high efficiency.
MANAGE
Manage the machine learning life cycle with tools to
collaborate, iterate, audit, secure, and govern.
Trusted for Mission Critical Applications
The AI Layer is trusted by Fortune 500 companies, the United
Nations, and thousands of users.
“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.”
Anna Patterson
VP of Engineering, AI at Google | Managing Partner, Gradient Ventures | Algorithmia Board
