Unveiling the Power of MLOps Solutions
The final goal of all machine learning (ML) projects is to develop ML products and productionize them rapidly for business use. But in the real world many ML models never see the production environment. According to Algorithmia, only 22% of the organizations that use ML have successfully put the models into production. And for the models that get into production, many companies lack an effective framework to manage the end-to-end life cycle of the deployed models and monitor them for reliability and accuracy. This is where Machine Learning Operations (MLOPs) steps in as a framework and manages every stage of the model life cycle starting from model deployment to model monitoring. In this blog we will discuss the top capabilities of MLOps, and we will also understand them in the context of Predactica’s Observability platform which is snowflake native and utilizes the latest implementation of Snowpark functionalities in managing the Model life cycle.
Snowpark, the groundbreaking feature of Snowflake, has empowered data engineers and data scientists with a unified platform to perform complex data transformations and analytics directly within Snowflake. While Snowpark offers unparalleled flexibility in data processing and advanced analytics, its native functionality does not encompass MLOps as a capability to the fuller extent. This leaves users seeking an external solution to integrate MLOps seamlessly into their Snowpark-driven ML workflows.
Predactica’s Observability Platform with its cutting-edge MLOps capability and Model explain ability features, seamlessly complements Snowflake by filling in this missing MLOps capabilities that the users are looking for. Predactica’s all-inclusive Observability platform empowers data-driven enterprises to harness the full potential of Snowflake and Snowpark while incorporating advanced MLOps functionalities to elevate their machine learning experience to the next level. Best of all, it is completely snowflake native and makes its adoption seamless for Snowflake Users.
Elevating MLOps with Comprehensive Features
1) Workflow Orchestration for MLOps
Most important feature of MLOps framework is its Workflow orchestration feature. All ML Models are associated with training and scoring pipelines that take in raw data and prepare it for Model training and Model scoring. The data preparation steps typically involve data cleansing, feature engineering and finally feeding the prepared data for model training or scoring. These discrete but interdependent steps, otherwise called tasks in the directed acyclic graph (DAG) are orchestrated as a workflow using tools such as Apache airflow, Kubeflow and scheduler native to Snowflake.
Predactica’s MLOps is designed to deploy and manage ML models that are both native to Predactica’s ML platform and models that are imported from outside. In the case of natively built models, each model is associated with its data prep pipeline, hyper parameters, and data preparation metadata. For externally imported models, the platform provides the option to import the pipeline associated along with the model binaries and register them to the model registry and metadata database.
Predactica’s workflow orchestration feature encapsulates all granular tasks as a sequence of steps and executes the workflow using Snowflake’s native orchestration tool wrapped with cloud native scheduler. For the user, once he/she deploys the models and sets up a prediction schedule, the end-to-end pipeline is executed as sequence of tasks inside Predactica’s snowflake workflow framework.
2) Model Versioning
For data, model and code, versioning is of paramount importance for doing auditing, to enable traceability and also to keep record for compliance and auditing reason. By providing the ability to compare different versions of models and their respective hyper parameter tuning parameters, version control enables organizations to streamline their model management and foster optimal decision-making processes.
Model versioning on Predactica ML Repository
Predactica’s MLOps framework helps in tracking and managing changes across multiple iterations of ML models. By maintaining a detailed history of model versions and hyperparameters, data scientists can easily revisit and compare the performance of various models. This invaluable insight aids in identifying the most successful model configurations and fine-tuning them for superior results.
Predactica’s model versioning feature provides quick and seamless access to compare deployed model versions and their respective performance metrics. Users can instantly view model accuracy, precision, recall, and other key metrics for each version, enabling them to identify trends, spot anomalies, and make timely adjustments. This accelerated access to model comparisons accelerates the model selection process and enhances the overall efficiency of the MLOps workflow.
3) Model Deployment
Predactica’s advanced Model deployment tool allows users to configure various mandatory and optional features including automatic drift detection and retraining. User could set the required threshold for data drift and threshold for individual model performance metrics inside the deployment UI. When these threshold values are met during pipeline execution, the MLOps engine automatically triggers model retraining. The user also has the option to choose the type of input he/she wants for the retraining. The retrained model can be manually deployed again for production use.
Model Deployment workflow inside Predactica’s MLOps product suite
4) Model Monitoring
Model metrics monitoring and alerting is essential for any MLOps tool as they provide valuable insights into the model’s behavior and its alignment with business objectives. Model performance could degrade over time for various reasons and failure to meet performance standards and thresholds can have severe consequences, leading to inaccurate predictions, decreased customer satisfaction, and missed opportunities.
With Predactica’s advanced monitoring capabilities, every deployed model’s performance is continuously evaluated against predefined business thresholds, ensuring that it meets the required standards. This feature empowers data scientists to optimize and fine-tune their models for superior results, thanks to the real-time monitoring of key metrics such as model accuracy, F1 score and precision. This feature also allows them to take proactive measures and make necessary adjustments when performance begins to deviate from desired levels.
One of many important reasons for model performance degradation is data drift, when new incoming data widely differs in distribution pattern from the original trained data. In such situations, Model retraining is advisable to make the model efficient and useful for the end users. Visible Bias in the model output could also be another trigger for Model retraining.
Model Retraining feature inside Predactica’s Model deployment flow
By providing comprehensive performance monitoring and automated retraining capabilities, Predactica’s Observability tool enables businesses to maintain peak model efficiency, make data-driven decisions with confidence, and deliver optimal results in a competitive landscape. With this Predactica platform’s support, businesses can unlock the full potential of their machine learning initiatives, driving growth and success.
5) Model Explainability
Model Explainability(XAI) provides answers to key business questions from Model inferences. Predactica’s Observability Platform captures the local and global explanation values and could also answer the reasons behind the prediction at each individual observation level. Global explanation provides insight into Feature importance at the global level as well. The tool also provides a what-if analysis feature that is extremely useful for the business users in decision making process.
Local Explanation and what-if analysis inside Predactica’s MLOps tool
6) Alert and Notification
Model management and monitoring needs an efficient and timely alert and notification module that notifies users on important events occurring in the ML life cycle. With this service, end users get communicated about any fatal failures in the operations and get communicated about important events such as triggering of a model retraining or occurrence of data drift over a period of Production runs.
Predactica’s Observability platform has exclusive alert and notification service that is designed to notify the users via email of any important event and at the same time lists all the alerts in its summary dashboard for users to acknowledge and act on them. The settings for the alerts and notification is configured during the deployment stage, when the threshold values for metrics to be monitored are set. For example, when the data drift is detected in the model dataset, the model retraining is triggered (if auto retraining is enabled) and corresponding email alerts are sent to the user. Apart from the communication, the alerts are also displayed in the summary dashboard for user acknowledgement.
In Conclusion, Predactica’s Observability platform is a full service MLOps platform that delivers end-to-end Model management capability to the end users and automates various critical stages of model management and monitoring so that data scientists and ML engineers alike can focus on model and pipeline build rather than being bogged down with operational challenges of managing Model lifecycle. For the fact that Predactica’s Observability platform is completely snowflake native, this comes as a natural choice for snowflake user. By leveraging Predactica’s Observability platform, snowflake users could do both model building, and model management/monitoring tasks, all under the same roof.