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Machine Learning and AI

This guide will help you get started with setting up and deploying a complete MLOps pipeline using key components such as Dataiku, Datapipeline, Label Studio, MLflow, JupyterHub, KServe, and OpenWebUI for testing LLMs (Large Language Models). Whether you are a data engineer or a machine learning engineer, this guide will walk you through the basics of building and deploying end-to-end ML workflows.

Prerequisites​

Before getting started, ensure that you have the following:

  • A user account with access to the tools mentioned (Dataiku, Datapipeline, Label Studio, MLflow, etc.).
  • The necessary permissions to create and deploy models and pipelines.

Setting Up the Environment​

Configure Dataiku​

Dataiku is a collaborative data science platform used for data preparation, machine learning model building, and automation.

  • Log in on Dataiku on your platform.
  • Create a new project where you will manage the data processing and model training tasks.
  • Use the Dataiku Flow to define your data pipelines, including importing, cleaning, and transforming the data that will be used for model training.

Set Up Label Studio for Data Labeling​

Label Studio is an open-source data labeling tool. It helps in creating labeled datasets for supervised machine learning tasks.

  • Log in on Label Studio on your platform.
  • Create a new labeling project within Label Studio for the task you want to label (e.g., image classification, text annotation).
  • Export labeled data in the appropriate format (JSON, CSV) for use in Dataiku.

Model Development and Training​

Use JupyterHub for Experimentation​

JupyterHub allows you to run and manage multiple Jupyter notebook environments for experimentation and model development.

  • Log in on JupyterHub
  • Use Jupyter Notebooks to write and test code for data processing, feature engineering, and machine learning model training.
  • Leverage libraries such as scikit-learn, TensorFlow, PyTorch, or any custom libraries needed for your specific ML tasks.

Model Training and Experiment Tracking with MLflow​

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, including experimentation, model tracking, and deployment.

  • Connect MLflow with JupyterHub to log experiments, including model parameters, metrics, and outputs.
  • Track multiple versions of the same model and easily compare different experiments to select the best-performing model.

Deploying and Serving Models​

Deploy Models with KServe​

KServe is an open-source framework that simplifies deploying machine learning models for inference on Kubernetes clusters.

  • Export the trained model from MLflow or Dataiku and deploy it using KServe.
  • The model API endpoint for serving predictions is automatically exposed and youc can integrate it with other applications.

Monitoring and Scaling with KServe​

Once the model is deployed, use KServe’s monitoring tools to track its performance and scale the service based on demand. You can set up autoscaling policies to ensure that the service remains responsive under high traffic.

Testing and Validating the Model​

Use OpenWebUI for Testing the LLM (Large Language Models)​

OpenWebUI is a web interface used to interact with and test machine learning models, including Large Language Models (LLMs). This step is crucial for validating your model's performance in a real-world setting.

  • Log in to OpenWebUI and configure it to communicate with the deployed LLM.
  • Use OpenWebUI to test your model by submitting input queries and validating the outputs.
  • Adjust the model or deployment configuration based on the results from OpenWebUI.

End-to-End Workflow and Automation​

Automate the MLOps Pipeline​

To make the MLOps pipeline more efficient, automate the workflow as much as possible using Dataiku’s Automation Engine or through Kubernetes CronJobs.

  • Schedule model training jobs in Dataiku.
  • Automatically trigger retraining when new data is added or when performance falls below a threshold.
  • Use MLflow for version control and automatic deployment of models when new versions are trained.

Conclusion​

Congratulations! You now have a basic understanding of how to integrate and deploy a complete MLOps pipeline using Dataiku, Label Studio, MLflow, JupyterHub, KServe, and OpenWebUI. This pipeline allows you to manage the entire lifecycle of a machine learning model—from data labeling and training to deployment and testing.

This guide provides an overview, but each of these tools has advanced features and capabilities to optimize your MLOps workflow further. Explore their documentation for more in-depth information on customization and scaling.

If you have any questions or need further assistance, feel free to consult the specific documentation for each tool.