Deploy your models.
Get your models into production and ready to scale with ease.
Git-push your pre-trained model, function, or algorithm, and the Artificial Intelligence Layer automatically creates a versioned, permissioned, scalable API endpoint any application or model can call.
Git-push your pre-trained model, function, or algorithm, and the Artificial Intelligence Layer automatically creates a versioned, permissioned, scalable API endpoint any application or model can call.
Related Resources:
Data Scientists Should Use the Best Tool for the Job
Publishing a Model
Deploy with minimal effort
Git-push your trained model or upload with our GUI and we’ll create a versioned, permissioned, scalable API endpoint that’s ready in seconds.
Version everything
Automatic model versioning enforces governance and consistency. Call any version of a model at any time.
Work with any popular framework
Tensorflow or PyTorch, Scikit-learn or MXNet–or hundreds of other combinations. Choose the frameworks and workbenches you need.
Tensorflow
Keras
SciKit-Learn
PyTorch
Caffe
OpenCV
Write in multiple languages
Python, Scala, R, Java, JavaScript, Ruby, and more. Choose the language that works best for each project.
Python, Scala, R, Java, JavaScript, Ruby, and more. Choose the language that works best for each project.
Python
JavaScript
R
Ruby
Scala
Java
Use GPUs or CPUs
Tune your ML portfolio for performance and cost, running each model on high-performance GPUs where you need it or inexpensive CPUs where you don’t.
Tune your ML portfolio for performance and cost, running each model on high-performance GPUs where you need it or inexpensive CPUs where you don’t.
CPU
GPU
Model pipelining
Build only what you need and reuse modular ML components across multiple jobs. Optimize performance by caching frequently used components and running each model on the right infrastructure for the job.
Build only what you need and reuse modular ML components across multiple jobs. Optimize performance by caching frequently used components and running each model on the right infrastructure for the job.
Advanced tech stack
Leverage the flexibility, compatibility, and performance of Git, Docker, Kubernetes, and other industry-leading infrastructure standards.
Leverage the flexibility, compatibility, and performance of Git, Docker, Kubernetes, and other industry-leading infrastructure standards.
GitHub
Docker
Kubernetes
Deployment coaching
Move ML off your laptop and into production with Model Deployment Coaching with a deployment engineer.
Move ML off your laptop and into production with Model Deployment Coaching with a deployment engineer.
- Training on how to get a model into production
- Complimentary AI Layer credits
- Use case definition and templates
- Model testing and benchmarking
- A personalized production plan
Enterprise
White glove engineering support
All AI Layer customers can request support via email or chat. Enterprise customers receive onramp training, access to a Learning Management System, a support SLA, a dedicated account manager, and priority support queueing.
All AI Layer customers can request support via email or chat. Enterprise customers receive onramp training, access to a Learning Management System, a support SLA, a dedicated account manager, and priority support queueing.
Enterprise
Model portfolio management
Centralize all your ML assets in a permissioned, searchable repository that includes documentation and live previews for each version of each model. Test the models inline and integrate them using preconfigured code snippets for almost any language.
Centralize all your ML assets in a permissioned, searchable repository that includes documentation and live previews for each version of each model. Test the models inline and integrate them using preconfigured code snippets for almost any language.
Connect to your data sources
and training platforms.
Connect to data in AWS, Azure, Google Cloud Platform, and SaaS-based Dropbox. With connectors for MS SQL Server, MySQL, and PostgreSQL, you can put your on-prem data to work without the cost and hassle of cloud migration. And if you need to access data stores outside of our connector library, our Data Portal makes it easy to build direct connections.
Related Resources:
Connecting Your Data
Your data, your cloud
Connect to data in AWS, Azure, Google Cloud Platform, and Dropbox.
AWS S3
Azure Blob
GCS
Dropbox
Integrate with your current workflows
Enable continuous integration and continuous delivery pipelines programmatically, via APIs, without interacting with the Algorithmia UI.
Sagemaker
Jupyter
KubeFlow
ML Flow
Tensorflow
Hub
Hub
Spark
H2O.ai
Deploy models from most data science workbenches
SageMaker, Kubeflow, TensorFlow Hub, Jupyter, MLFlow, Spark, H2O.ai, and more…
Enterprise
Work with custom data sources
If you need to access data stores outside of our connector library, our Data Portal makes it easy to build direct connections.
If you need to access data stores outside of our connector library, our Data Portal makes it easy to build direct connections.
Enterprise
Leverage your enterprise data sources
Easily connect to any database with custom data connectors, so you can put your on-prem data to work without the hassle of cloud migration.
Easily connect to any database with custom data connectors, so you can put your on-prem data to work without the hassle of cloud migration.
Scale effortlessly.
The Artificial Intelligence Layer’s predictive scaling keeps compute resources running at peak efficiency, and intelligent caching and low latency let you serve custom multi-model pipelines on demand under any load. There’s nothing to configure or provision. Just deploy your models and we handle the rest.
Related Resources:
Lessons Learned from Deploying Deep Learning at Scale
Custom Prometheus/Grafana Charts
High-scale model serving
Deploy and get back to work. The AI Layer automates the processes and infrastructure necessary to serve any number of models and meet any demand with patent-pending serverless technology that runs across CPUs and GPUs.
Trusted cloud
Serve your models on the world’s first and largest public ML platform and marketplace, trusted by more than tens of thousands of developers with thousands of models. Enterprises can run the AI Layer behind their firewall or have Algorithmia host.
Scale elastically
Scale across CPUs and GPUs to meet demand, then release unused resources to reduce costs.
Enterprise
Advanced scheduling
Maximize hardware investments. Densely scheduled jobs keep your infrastructure running at peak capacity.
Maximize hardware investments. Densely scheduled jobs keep your infrastructure running at peak capacity.
Enterprise
Run on any compute
Public cloud. Private cloud. Even fully air-gapped. Serve your models on the right compute platform for your business.
Public cloud. Private cloud. Even fully air-gapped. Serve your models on the right compute platform for your business.
Private
AWS
On-prem
Azure
Air-gapped
Enterprise
Custom monitoring
Monitor and manage the health and performance of your ML system, from system-wide metrics down to individual models, users, or API calls.
Monitor and manage the health and performance of your ML system, from system-wide metrics down to individual models, users, or API calls.
Manage the ML life cycle.
Automate everything that happens after you deploy your model.
ML is a process, and the Artificial Intelligence Layer automates the hassle of managing iterations. Model versioning, discovery, evaluation, and sharing are all built into the system, as is detailed logging. When you need an audit trail, you can access every API call, so you always know which user called which version of which algorithm and with what data.
Related Resources:
Model Evaluations Beyond Compare
Model Overview
Catalog your ML portfolio
Centralize all your ML assets in a single, searchable repository that includes documentation and live previews for each version of each model.
Model Evaluation Results
Evaluate performance and accuracy
Run multiple models at once on the same infrastructure and dataset, comparing results, errors, drift, and performance side by side, so you’ll always know you’re running the best model for any job.
Increase efficiency with pipelining
Build multi-model workflows of independent, modular components. Choose the best components for any job and maximize code reuse across your organization.
Security
Your data and operations are always secure. Every API call runs in its own container and memory space so there’s no risk of accidental data sharing. Data is always encrypted, and we provide full support for any encryption offered by other infrastructure providers.
Your data and operations are always secure. Every API call runs in its own container and memory space so there’s no risk of accidental data sharing. Data is always encrypted, and we provide full support for any encryption offered by other infrastructure providers.
Govern model access
Use your organization's auth system to manage users and access on the AI Layer.
Use your organization's auth system to manage users and access on the AI Layer.
Enterprise
Logging and auditability
The AI Layer provides a comprehensive audit trail, logging every API call. Know which user called which version of which algorithm and version, and capture additional data if necessary.
The AI Layer provides a comprehensive audit trail, logging every API call. Know which user called which version of which algorithm and version, and capture additional data if necessary.
Enterprise
Custom Auth
The AI Layer’s authentication and authorization is fully customizable, supporting SSO and a variety of directories and identity platforms, including Open ID Connect, LDAP, and more.
The AI Layer’s authentication and authorization is fully customizable, supporting SSO and a variety of directories and identity platforms, including Open ID Connect, LDAP, and more.
The AI Layer allows you to pick and choose the best model and infrastructure for the job.
The AI Layer allows you to pick and choose the best model and infrastructure for the job.
