MLOps With Dataiku: The Key to Scalable ML Models

Deploying Projects to Production

Introducing MLOps with Dataiku is easy. The deployer is the central place where operators can manage versions of Dataiku projects and API deployments across their individual life cycles.

Manage production environments as well as code environment and infrastructure dependencies for both batch and real time scoring, and deploy bundles and API services across dev, test, and prod environments for a robust approach to updates in your machine learning pipelines.

Reliable Batch Operations

Dataiku automation nodes are dedicated production servers that execute scenarios for everyday production tasks like updating data, refreshing pipelines, and MLOps monitoring (monitoring of ML models or retraining models based on a schedule or triggers).

With extensive deployment capabilities, data scientists & ML engineers are now enabled to deploy API services created in Dataiku on platforms beyond Dataiku API nodes, including AWS Sagemaker, Azure Machine Learning, and Google Vertex. This capability extends the reach and flexibility of API deployment, providing seamless integration with various external platforms.

With these dedicated execution servers, multiple AI projects run smoothly in a reliable and isolated production environment.

Real-Time Results with API Services

With Dataiku you can create a centralized, real-time MLOps landscape. Deliver answers on-demand with Dataiku API nodes —- elastic, highly available infrastructure that dynamically scales cloud resources to meet changing needs.

In just a few clicks, generate REST API endpoints for real-time model inference, Python functions, SQL queries, and dataset lookups, leading to more downstream applications. A feedback loop, and processes powered by AI.

Model Retraining and Comparisons

Production models periodically need to be updated based on newer data or shifting conditions. Teams may either manually refactor a model or set up automated retraining based on a schedule or specific triggers, such as significant data or performance drift.

With comprehensive model comparisons in Dataiku, data scientists and ML engineers perform champion/challenger analysis on candidate models to make informed decisions about the best model to deploy in production.

CI/CD with APIs for DevOps

Bring DevOps tools into the MLOps world. Robust APIs enable IT and ML engineers to programmatically perform Dataiku operations from external orchestration systems and incorporate MLOps tasks into existing data workflows. Dataiku, as an open MLOps platform, integrates with the tools that DevOps teams already use, like Jenkins, GitLabCI, Travis CI, or Azure Pipelines. 

Model Stress Tests and Auto-Documentation

Data preparation and data discovery are two of the main pillars that the MLOps life cycle operates around. Successful MLOps use cases include data preparation and data discovery as a central focus. MLOps best practices are dedicated to ensuring data quality. With a series of stress tests simulating real world data quality issues, ML engineers/ MLOps architects reduce risk by assessing model robustness and behavior under adverse conditions, prior to deployment. Deploy models that you know will be reliable with Dataiku. 

Automatically-generated, customizable documentation for models and pipelines helps teams retain critical project context for reproducibility and compliance purposes while simultaneously reducing the burden of manual documentation.

By working out-of-the-box and automatically aggregating multiple types of monitoring in a single place (activity, deployment, execution, model), Unified Monitoring in Dataiku acts as a central cockpit for all your MLOps activity in one place. It becomes your one-stop solution for tracking the health of AI models across diverse origins, from projects and APIs to cloud endpoints like AWS Sagemaker, Azure Machine Learning, and Google Vertex AI.