Aqueduct is an MLOps framework that allows you to define and deploy machine learning and LLM workloads on any cloud infrastructure.

Getting Started with Aqueduct

If you’re eager to dive into the world of machine learning operations (MLOps) using Aqueduct, here’s how to do it:

bash
pip3 install aqueduct-ml
aqueduct start

How It Works

Aqueduct’s Python native API allows you to define ML tasks in regular Python code. Imagine you are a chef preparing a meal in a kitchen where you have all your ingredients at hand and the right cooking tools. Here’s how the components work together:

• Ingredients (Code): Like recipes, you write your tasks in Python, defining your workflow.
• Cooking Stations (Cloud Infrastructure): Your code can seamlessly move from one cloud service to another, like preparing different dishes in various parts of your kitchen.
• Chef’s Performance (Execution Monitoring): You can monitor how well your ‘cooking’ (model execution) is doing, ensuring everything comes out delicious (accurate and efficient).

For example, you might want to create a pipeline that trains a model on Kubernetes using a GPU and validates that model in AWS Lambda. Here’s how you can do that:

python
# Use an existing LLM.
vicuna = aq.llm_op(vicuna_7b, engine='eks-us-east-2')
features = vicuna(
    raw_logs,             
    prompt='Turn this log entry into a CSV: text'
)

# Or write a custom op on your favorite infrastructure!
@op(
    engine='kubernetes',  # Get a GPU.  
    resources={'gpu_resource_name': 'nvidia.com/gpu'}
)
def train(featurized_logs):
    return model.train(features)  # Train your model.

train(features)

Why Choose Aqueduct?

Aqueduct stands out in the MLOps landscape for several key reasons:

• Python-native pipeline API: Quickly get your code into production without cumbersome configurations.
• Integrated with your infrastructure: Easily run on existing cloud tools, no rip-and-replace necessary.
• Centralized visibility: Monitor workflows for performance and issues effectively.
• Security: Runs securely on your cloud, ensuring your data remains protected.

Exploring Examples

Aqueduct allows you to build workflows that can be run on a fixed schedule or triggered on-demand. You can see Aqueduct’s real-world applications through various examples such as:

• Churn Ensemble
• Sentiment Analysis
• Impute Missing Wine Data
• … and more!

Troubleshooting Tips

If you encounter any issues while setting up or running your workflows, here are some troubleshooting ideas:

• Check that your cloud infrastructure is correctly configured and has the necessary resources.
• Ensure that your Python environment has all required packages installed.
• Look at Aqueduct documentation for detailed guides and examples.
• For specific bugs, consider checking and reporting them on our GitHub Issues page.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Wrapping Up

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.