Introducing Aqueduct: A Seamless MLOps Framework
Aqueduct is an innovative open-source MLOps framework that simplifies the deployment of machine learning (ML) and large language model (LLM) workloads across any cloud infrastructure. Ideal for data scientists and engineers, Aqueduct facilitates smooth transitions from local to cloud-based model execution.
Key Features
Python Native API
Aqueduct allows users to define machine learning tasks using standard Python code. Its Python-native pipeline API eliminates the need for learning complex domain-specific languages or managing cumbersome YAML files. This makes getting code into production fast and efficient.
Cloud Integration
With Aqueduct, users can leverage their existing cloud infrastructure, such as Kubernetes, Spark, or AWS Lambda, without the hassle of replacing current systems. This means ML projects gain enhanced flexibility and performance without additional overhead.
Centralized Monitoring
Once workflows are operational, Aqueduct provides real-time visibility into the data, metrics, and metadata generated by each run. This centralized overview helps users quickly identify any issues, ensuring pipeline reliability and efficiency.
Security and Open Source
Aqueduct is entirely open-source and operates securely within the user's cloud environment. As a result, users can maintain confidence in data security and privacy while benefiting from a robust community-driven support system.
How Aqueduct Works
Starting with Aqueduct is straightforward. Users can quickly set up the environment using just a couple of command lines:
pip3 install aqueduct-ml
aqueduct start
Once installed, the platform allows code developed on a local machine to be seamlessly transitioned to a preferred cloud infrastructure. Aqueduct's ability to move workloads between different cloud layers, such as training models on GPUs with Kubernetes and validating them through AWS Lambda, exemplifies its versatility.
Why Choose Aqueduct?
Aqueduct addresses the complexities of machine learning operations, providing an all-encompassing solution for managing diverse and siloed infrastructures. It presents a unified interface to handle ML tasks while granting comprehensive visibility and control over code execution across multiple systems.
The framework is especially valuable in environments where teams must juggle various cloud toolsets, offering a streamlined experience that enhances productivity and collaboration. Its lack of reliance on specific infrastructures allows teams to implement Aqueduct without disrupting current workflows.
Examples and Applications
To explore Aqueduct's capabilities, a variety of real-world machine learning examples are available, such as:
These examples provide clear insights into how Aqueduct operates across different ML scenarios.
Next Steps
For those interested in diving deeper, Aqueduct's comprehensive documentation offers a wealth of resources, including:
- A detailed Quickstart Guide
- Extensive example workflows
- In-depth information on creating workflows
Engage with the community or contribute by exploring the roadmap and joining the development conversations on Slack or GitHub. Aqueduct is an evolving platform, and community input is both valued and encouraged.