A Brief Introduction to MLOps
Machine Learning Operations (MLOps) is the practice of building, deploying, and maintaining machine learning models efficiently and reliably. Inspired by DevOps principles, MLOps incorporates Continuous Integration, Continuous Deployment, Continuous Training/Testing and Continuous Monitoring (CI/CD/CT/CM) practices to ensure that machine learning models transition seamlessly from development to production, where they can serve users effectively.
Databricks, in their Big Book of MLOps (I highly recommend you request a copy here!), define MLOps as the practice of managing data, code and models, giving the equation:
MLOps = DataOps + DevOps + ModelOps
A typical MLOps cycle. src: https://www.databricks.com/glossary/mlops
Integrating robust MLOps practices into machine learning projects is essential for success. Key benefits include:
- Scalability: Handle increasing amounts of data and users without sacrificing performance.
- Reproducibility: Ensure consistency in data and models across environments.
- Automation: Streamline repetitive tasks like data ingestion and pre-processing, reducing errors and saving time.
- Data Governance: Monitor the lifecycle of data and models to ensure compliance with regulatory standards.
- Monitoring: Quickly identify and resolve issues causing performance degradation, keeping models effective.
As machine learning workflows grow more complex—with increasing data volumes and real-time model demands—adopting MLOps practices becomes crucial. However, selecting the right tools and ensuring seamless integration can be a challenge.
In this blog, we explore why Databricks stands out as the ultimate platform for MLOps. By unifying best-in-class MLOps tools with its powerful Data Lakehouse architecture, Databricks enables organisations to build scalable, efficient, and reliable machine learning workflows with ease.
Databricks and Unity Catalog: Your Foundation for Unified Data and MLOps
Databricks takes a data-centric approach, unifying data engineering, data science, and data analytics into a versatile platform capable of managing the entire data lifecycle for both structured and unstructured data. At the core of this unified platform is Unity Catalog, Databricks' governance solution. Unity Catalog provides centralised access control, auditing, lineage tracking, and data discovery across workspaces, enabling extensive collaboration throughout the data lifecycle.
For example, teams can use Unity Catalog to restrict access to sensitive training data while maintaining a full audit trail, ensuring compliance with data privacy regulations. This level of governance is critical for industries such as healthcare and finance, where data security is paramount.
Unity Catalog further enhances the machine learning lifecycle by extending its governance and management properties to AI assets such as models, features, and pipelines. This ensures transparency, traceability, and collaboration, allowing data scientists to track model versions, debug issues, and ensure reproducibility.
Databricks is powered by Apache Spark, enabling highly scalable data processing and compute. Its optimised Spark runtime significantly improves the speed and efficiency of machine learning workflows by distributing workloads across compute clusters. This scalability is particularly valuable for hyperparameter tuning, large-scale model training, and batch inference.
Databricks integrates Unity Catalog with other powerful tools such as MLflow, Feature Store, and Model Serving. Together, these tools provide a comprehensive MLOps environment that simplifies model development, deployment, and governance. For more information on Unity Catalog, check out this blog!
Feature Store: Consistent Features for Scalable ML
Machine learning models take data as input and learn from distributions to make predictions for specific inputs. Clean, processed data is crucial for improving the accuracy and reliability of machine learning models. In fact, the quality of feature engineering often determines whether a machine learning solution will succeed in improving business outcomes or fail to deliver value. However, achieving consistent results can be time-consuming and challenging without a centralised approach.
Feature stores address this challenge by serving as centralised repositories that enable data scientists to share and reuse features across teams and projects. They ensure that feature values remain consistent between model training and inference, reducing the need for redundant work like rerunning data processing pipelines.
In Databricks, a feature store table is implemented as a Delta table with primary key(s), allowing for the joining of multiple feature tables and integration into machine learning workflows. These feature store tables contain predictive variables that can be used for both training and inference. They are tightly integrated with Unity Catalog for access control, lineage tracking, and auditing, ensuring governance and transparency. Additionally, they connect seamlessly with other MLOps tools such as MLflow for experiment tracking and Model Serving for real-time or batch inference, creating an end-to-end solution for machine learning operations.
An example of joining two feature store tables to form a training dataset for an ML model. Src: https://docs.databricks.com/en/machine-learning/feature-store/concepts.html
The Databricks Feature Store plays an essential role in scalable machine learning solutions by reducing complexity and improving collaboration. For example, in a recent customer churn project, the team at Advancing Analytics implemented a feature store using multiple primary keys to allow additional data sources to be integrated into the pipeline seamlessly. As a result, the customer was able to scale their workflows while maintaining consistency and efficiency across the machine learning lifecycle.
Beyond this, the Feature Store enables real-time feature serving, which is critical for applications such as fraud detection, dynamic pricing, and personalised customer experiences. By ensuring consistency, reusability, and transparency, the Databricks Feature Store enables teams to build reliable and scalable machine learning pipelines, earning its place as a cornerstone of MLOps on Databricks.
MLflow: Simplify Model Development and Tracking
MLflow is an open-source platform designed to address common challenges that arise during machine learning model development and deployment. These challenges include tracking model metrics and parameters, managing model versions, and deploying models across different environments. MLflow ensures that every stage of the machine learning lifecycle is tracked, enabling traceable and reproducible results. This not only improves explainability but also fosters greater collaboration across data science teams.
MLflow consists of several key components:
- Tracking: MLflow Tracking allows users to log and query model parameters, metrics, and artifacts throughout the full lifecycle of a machine learning project. It enables comparisons between different model experiments, providing deeper insights into performance and helping data scientists select the best-performing models.
- Models: The MLflow Models component standardises machine learning models into a format that can be easily shared and deployed across a variety of environments. This standardised packaging supports major machine learning libraries, including PyTorch, TensorFlow, and Scikit-learn, making deployment seamless regardless of the framework used for training.
- Projects: MLflow Projects enable reproducible machine learning workflows by standardising environments and code execution. This ensures that teams can easily share and run code in a consistent manner across different environments, enhancing collaboration and reducing errors caused by dependency mismatches.
- Model Registry: The Model Registry is a centralised repository for managing machine learning models throughout their lifecycle. It supports model versioning, model staging (e.g., transitioning models between Staging, Production, and Archived states), and tracking metadata such as model lineage and approval workflows.
Databricks provides a fully-managed version of MLflow, allowing users to leverage either the UI or the API for tracking model development. This managed environment simplifies the setup and maintenance of MLflow, enabling data scientists to focus on experimentation and model optimisation.
An example of the MLflow UI for experiment tracking, in this case the results of an AutoML experiment.
Additionally, MLflow integrates seamlessly with Unity Catalog, providing a centralised model repository with governance features. This integration provides secure access and enables teams to run experiments on models across different workspaces while maintaining compliance and traceability. Furthermore, Unity Catalog integration ensures governance is applied to models and regulatory compliance is easily achieved.
MLflow also integrates with the Databricks Feature Store, offering automated feature lookups during model inference. This eliminates the need for custom pipelines to fetch features, ensuring consistency between feature engineering in training and inference pipelines. For example, a real-time fraud detection model can leverage the Feature Store to retrieve up-to-date transaction features with minimal latency.
In a customer churn prediction project, MLflow was essential for tracking model runs across all development stages, from experimentation to production. I used MLflow Tracking to log hyperparameters, evaluation metrics, and feature importance, enabling easy model comparison. Additionally, I logged SHAP scores to track feature impact, enhancing model explainability and debugging. This workflow improved reproducibility, governance, and deployment efficiency, making MLflow a key component of the MLOps process.
Databricks seamlessly integrates MLflow’s powerful features with its platform, enabling effortless and streamlined machine learning workflows. Model development is simplified through comprehensive experiment tracking, allowing baseline models created with AutoML to be evaluated, hyperparameter tuning experiments to be logged, and model training processes to be easily monitored. Additionally, model deployment is streamlined with the Model Registry and MLflow’s standardised model packaging, ensuring that models can be efficiently transitioned from development to production. This combination of tools allows teams to maintain consistency, scalability, and reproducibility throughout the entire machine learning lifecycle.
Model Serving: Effortless and Scalable Deployment
Once a model is trained, tuned, and tested, it must be deployed and made accessible to its end-users. However, this process often presents several challenges, such as addressing high latency, building reliable infrastructure to ensure consistent service, and managing multiple model versions effectively.
Databricks Model Serving provides a robust solution to alleviate these common issues faced by ML engineers during model deployment. It offers a fully-managed, serverless environment for seamless model deployment, allowing data scientists and ML engineers to focus on optimising model performance rather than dealing with infrastructure complexities.
Model Serving supports real-time inference through REST API endpoints, making it easy to integrate with applications. The integration with MLflow’s standardised model packaging simplifies deployment for models registered in the Model Registry. These REST API endpoints can dynamically scale to handle varying workloads thanks to the auto-scaling feature. This ensures cost-efficiency during periods of low demand and optimal performance during peak usage.
Model Serving is deeply integrated with Unity Catalog, extending governance capabilities to deployed models. Through Lakehouse Monitoring, lineage between data and AI assets is maintained, ensuring transparency and accountability. For instance, inference tables automatically log inputs and outputs for each prediction request, enabling traceability and aiding compliance with regulatory requirements.
Unlike other platforms, such as Azure ML, Databricks Model Serving eliminates the need for managing containers or orchestration systems. The integration with MLflow significantly simplifies the deployment process and reduces the time required to move models from development to production.
The UI for creating a real-time model serving endpoint is incredibly simple!
Model Serving integrates seamlessly with other Databricks tools, such as Unity Catalog and MLflow, creating a cohesive and powerful ecosystem for MLOps. This unified approach enables data science teams to rapidly move models from development to deployment, freeing up resources to focus on optimisation and driving business outcomes.
By addressing the common pain points of model deployment, Databricks Model Serving empowers organisations to achieve scalable, reliable, and efficient production pipelines. The managed infrastructure not only accelerates time to production but also ensures that models are deployed with the governance and scalability required to meet business demands.
Workflows: Automation and Scalability for Your ML Pipelines
Databricks Workflows is a fully managed orchestration tool that automates machine learning and data pipelines. Users can create workflows programmatically or through the intuitive UI, making it accessible to both data engineers and data scientists. Workflows allow users to schedule, monitor, and manage various processes, from ETL pipelines to model training and inference, ensuring that machine learning systems run efficiently with minimal manual intervention.
As data environments grow increasingly complex, pipeline orchestration becomes essential for maintaining consistency, efficiency, and reliability. Automated workflows eliminate the need for manual execution, reducing errors and ensuring models are retrained and updated seamlessly. This is particularly crucial for batch inference pipelines, where models must process large volumes of data at scheduled intervals. By automating these workflows, ML engineers can shift their focus from managing inference jobs to monitoring model performance and data quality metrics.
Workflows have several key benefits for MLOps:
- Integration with the Databricks Ecosystem: Workflows can link various Databricks tools, such as automating feature creation and registering them in the Feature Store, ensuring consistency in ML pipelines. Also, integration with MLflow allows models to be trained, logged, and deployed in a structured, repeatable manner.
- Monitoring and Observability: Workflows provide rich monitoring capabilities, allowing teams to track execution progress, visualise dependencies, and log performance metrics. Alerts and notifications help teams quickly detect and resolve failures, improving operational efficiency.
- Built-in Error Handling: Errors are elegantly handled with automatic retries and failure notifications, preventing workflow disruptions. Workflows support fault-tolerant execution, ensuring that failed tasks do not cause entire pipelines to stop unexpectedly.
- Scalability and Performance Optimisation: Workflows are designed to scale seamlessly with increasing workloads, enabling organisations to process large datasets efficiently. Parallel execution capabilities allow multiple tasks to run concurrently, reducing processing time and improving throughput.
Databricks Workflows simplify the automation of tasks and subtasks. Src: https://www.databricks.com/blog/modular-orchestration-databricks-workflows
Next steps: Deep Dives into Best Practices for ML on Databricks
Databricks provides the ideal environment for managing the entire machine learning lifecycle—from data ingestion and feature engineering to model training, deployment, and automation. The tools discussed in this blog are just a glimpse of what Databricks offers to simplify MLOps, enabling scalability, efficiency, and governance without the burden of managing traditional infrastructure.
This blog serves as a starting point for understanding the power of Databricks in MLOps. Over the coming months, I will be publishing in-depth guides exploring each of these tools in more detail, complete with coding examples and best practices to help you maximise the value of this platform. Whether you’re a data scientist looking to streamline model training with MLflow or an ML engineer aiming to automate batch inference, these blogs will offer practical insights and implementation strategies.
The next blog in the series will focus on Databricks Feature Store best practices, covering how to efficiently store, manage, and retrieve ML features for consistent, scalable machine learning workflows. Stay tuned!
Have questions or want to dive in deeper? Contact us today and let’s discuss how we can help you optimise your MLOps with Databricks!
*Disclaimer: The blog post image was generated by AI and does not depict any real person, place, or event.
