Untangle the MLOps Knot
MLOps is a tangled mess of infrastructure, where every system has bespoke APIs and assumptions. The MLOps Knot is a nightmare to maintain and to add new tools into. Worst yet, the lack of proper sharing of code, data and metadata among these systems means teams are unable to keep track of what code is running, where it's running, or if it's functioning as expected.
This leads to delays in development cycles and wasted resources, ultimately making it hard for businesses to deliver on the value of machine learning.
Aqueduct:Â Virtualized ML Infrastructure
From Dev to Production with Python
Aqueduct enables you to move pipelines from development to production quickly and easily. You can define pipelines in concise Python code instead of having to write lengthy YAML configs or repetitive Dockerfiles. Aqueduct also automatically captures and recreates your environment (Python version, library dependencies, etc.) in the cloud, so you don't have to spend time debugging configuration issues or library version mismatches.
Run Seamlessly on your Existing Infrastructure
Forget having to fiddle with tedious cloud configuration settings and spending days trying to figure out how to deploy on Kubernetes clusters, AWS Lambda functions, or Apache Spark clusters — Aqueduct does the hard work for you. By automatically translating your pipeline code into the appropriate format and taking care of the deployment and orchestration, Aqueduct streamlines infrastructure management so you can focus on doing what matters most: building state-of-the-art machine learning models.
Deep Visibility into Pipeline Metadata
Once pipelines are in production, you need visibility into what pipelines are running and whether they are performing as anticipated. Aqueduct automatically captures and versions code and data at each step of the pipeline which helps ensure accuracy and speed up debugging. Aqueduct also enables you to define metrics and checks that continuously measure and validate your pipelines, so you can have peace of mind knowing that you have complete control.
Your data, secure in your cloud
With Aqueduct's open-source orchestration layer, you can be confident that all your code and data remain within your cloud. Furthermore, Aqueduct automates credential management by providing a unified way to access sensitive keys instead of having them scattered around in your code.
Coming Soon
On-demand compute integrations
Use Aqueduct to create cloud resources on the fly. Get access to Kubernetes and Spark clusters when you need them — in your cloud!
Dynamic DAGs
Construct workflows that define their task graph at execution time for highly scalable tasks like hyperparameter search.
Real-time prediction serving
Deploy your models to easily-managed, highly-scalable REST endpoints in your cloud.
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