Model Deployment
Sparkflows enables models to be deployed in the development environment and consequentially pushed to production environment via one click from the Models page. Deploying models to production was never so seamless.
Administer production environments alongside code environment and infrastructure dependencies for both batch and real-time scoring. Deploy bundles and API services across development, testing, and production environments to ensure a resilient approach to updates in your machine learning pipelines.
By facilitating quicker experimentation, deployments, and comprehensive lineage tracking, Sparkflows enhances the overall efficiency of ML workflows.
Model Registry
Sparkflows enables the feature engineering pipeline and the ML model to be registered in a model registry. One can use Sparkflows as a Model registry or MLflow can be leveraged to be used as model registry.
The Model Registry is a centralized repository for storing, organizing, and managing machine learning models. It plays a crucial role in the lifecycle of machine learning projects, ensuring that models can be easily tracked, versioned, validated, and deployed.
After evaluating models against each other, one logs the top-performing model(s) in the Model Registry in preparation for deployment. Leverage multiple iterations of models of the same type through Model Versioning.
Model Deployment for Batch Scoring
Sparkflows workflows and nodes execute tasks essential for routine production activities like data updates, pipeline refreshes, and MLOps monitoring, which includes overseeing ML models or scheduling model retraining.
With robust deployment capabilities, data scientists and ML engineers can extend the deployment of services created within Sparkflows to cloud platforms like AWS Sagemaker, Azure Machine Learning, and Google Vertex, Kubeflow widening the range and flexibility of API deployment and facilitating seamless integration with various external platforms.
This enables users to deploy models on any cloud of their choice and leverage it for Batch scoring on bulk data.
Model Deployment for Real Time Scoring
Model Deployment for Real Time Scoring
Utilizing Sparkflows, one can establish an MLOps environment that operates in real-time. Prompt responses are facilitated through Sparkflows, which offer highly available infrastructure and dynamically scale cloud resources on AWS Sagemaker, Azure Machine Learning, and Google Vertex, Kubeflow among others.
On deployment, you get a REST API endpoint for real-time model inference. This end point can be invoked via Sparkflows monitoring end point, via curl, third party API, Python requests or via any other programming language. This capability supports the development of applications and use cases which leverage these deployed models.
Model Monitoring
Sparkflows provides single pane view to monitor different metrics of models deployed. This enables to keep a tab on models performance over a period of time by computing and monitoring metrics like drift, latency among others. The models deployed are associated with thresholds and alerts on different metrics. When metrics cross the thresholds, alerts are captured by the MLOps system and relayed to users via emails or downstream APIs are triggered if setup.
In Sparkflows, model assessment repositories record and illustrate performance metrics, guaranteeing the sustained delivery of top-notch results from live models. Should a model's performance decline, integrated drift analysis aids operators in identifying and examining potential instances of data, performance, or prediction drift, allowing for informed decision-making moving forward.
Data Drift Monitoring
When the distribution of incoming data shifts away from the training dataset, models may deliver less accurate predictions — a phenomenon known as data drift. Sparkflows continuously monitors feature distributions and compares them against training baselines using metrics such as the Population Stability Index (PSI) and other statistical measures. Interactive visualizations highlight exactly when and where changes occur, enabling teams to identify potential issues early and take proactive steps before performance declines.
Model Performance Drift
Even when input data remains consistent, models can lose effectiveness as patterns evolve over time. Sparkflows tracks model performance metrics such as accuracy, precision, recall, and F1-score against baselines captured during training. Clear dashboards and charts make it simple to spot dips in predictive power, pinpointing when a model begins to underperform. This visibility ensures data science and ML teams know precisely when to retrain or adjust models to keep them aligned with business needs.
Automated Retraining and CI/CD Pipelines
Monitoring alone is not enough — action is key to keeping models reliable. Sparkflows enables policy-driven retraining, where workflows are triggered automatically if drift thresholds are crossed, or on a scheduled basis (e.g., monthly or quarterly). Teams also receive alerts for anomalies, ensuring transparency and faster response.
Retraining pipelines integrate seamlessly with enterprise CI/CD systems like Jenkins, GitLabCI, Travis CI, or Azure Pipelines. This ensures models are refreshed regularly, aligned with DevOps best practices, and able to adapt quickly to new data. Time-based retraining can also be configured when periodic updates are required, ensuring models remain accurate and production-ready.
Model Governance, Compliance and Explainability
Sparkflows MLOps ensures data integrity and traceability throughout the entirety of the machine learning journey, allowing for seamless tracking from data acquisition, experimentation, model registration, to deployment.
The IP's used to invoke the deployed models are also tracked. It also captures details like who deployed the model and what changes were made to the deployment over a period of time. Each activity in the MLOps system is tracked and can be seen in audit logs and dashboards.
Model Formats
Sparkflows supports storing, registering, deployment of most of the open model formats like PMML, H2O MOJO, Pickle files, MLeap among others.
Each of the above formats have their tradeoffs - some are platform independent, some have super fast scoring latency, some are more widely adopted. Sparkflows allows you to pick and choose the format in which you want to save the model provided the underlying algorithm supports the format.
