One of the challenges to understanding MLOps is that the term itself is used very loosely in the ML community. In general, we should think about MLOps as an extension of DevOps methodologies but optimized for the lifecycle of ML applications. This definition makes perfect sense if we consider how fundamentally different the lifecycle of ML applications is compared to traditional software programs. For starters, ML applications are composed of both models and data, and they include stages such as training, feature engineering, hyperparameter optimization etc. that have no equivalence in traditional software applications.
Just like DevOps, MLOps looks to manage the different stages of the lifecycle of ML applications. More specifically, MLOps encompasses diverse areas such as data/model versioning, continuous integration, model monitoring, model testing, and many others. In no time, MLOps evolved from a set of best practices into a holistic approach to ML lifecycle management.
Fundamentally, MLOps is based on the following principles:
- One machine learning platform for many learning tasks: Providing a consistent architecture for automating the lifecycle of different types of machine learning models.
- Continuous training: Support a pipeline for enabling continuous training workflows of models.
- Easy-to-use configuration and tools: Configuration management is essential to automating the lifecycle of machine learning models.
- Production-level reliability and scalability: Having a series of building blocks to ensure that models built can operate at scale in production.
MLOPs Key Components
MLOps includes several key components of the lifecycle of machine learning models.
1. Model Monitoring
Considered by many to be the cornerstone of MLOps, model monitoring is one of the essential building blocks of any ML pipeline. In some ways, ML monitoring can be viewed as the next phase of the application performance monitoring (APM) space that has accompanied the evolution of software technology trends. ML is sufficiently unique that is likely to create a new generation of monitoring platforms that are specifically optimized for the performance of ML models.
In data science, models are not static artifacts but entities that require constant oversight. Model monitoring serves as the vigilant guardian, providing insights into the performance and behavior of deployed models in production environments. By monitoring key metrics and detecting anomalies, organizations can proactively identify issues such as model drift, data drift, thus mitigating potential risks and ensuring optimal performance over time. Additionally, tracking auxiliary metrics like latency and throughput offers insights into operational efficiency.
2. Feature Stores
Feature stores have rapidly become a key component of MLOps infrastructures. This is not surprising if we consider that many challenges in the lifecycle of ML models revolve around data and features. In any large ML team, data scientists spend most of their time extracting, selecting, and transforming data into features and then figuring out how to incorporate those features into production-ready ML models. From an ML architecture standpoint, a feature store can be seen as the missing link between feature engineering and feature serving. They facilitate collaboration and knowledge sharing among data scientists by enabling the reuse of curated features across machine learning models. This not only accelerates development cycles but also ensures consistency and reproducibility in model training.
The main capabilities include Feature Transformation, Feature Storage, Feature Serving, feature versioning, usage tracking, and lifecycle monitoring.
3. Model Serving
Model deployment/serving can be considered one of MLOps pipelines most challenging aspects. This is partly because model serving architectures have little to do with data science and are more related to ML engineering techniques. It involves deploying machine learning models into production environments, where they can generate predictions in response to incoming data. This process requires careful consideration of factors such as scalability, latency, and reliability to ensure optimal performance and user experience.
Some ML models take hours to execute, requiring large computation pipelines, while others can be executed in seconds on a mobile phone. A solid ML serving infrastructure should be able to adapt to diverse requirements from ML applications. Leveraging technologies such as containerization and microservices architecture, data scientists can deploy models at scale while maintaining flexibility and agility.
4. Model Packaging
Model packaging involves encapsulating trained models along with any necessary dependencies into a portable format suitable for deployment across diverse environments.
Effective model packaging facilitates seamless integration of machine learning models into production systems, minimizing compatibility issues and deployment complexities. Technologies such as Docker containers have emerged as popular solutions for packaging machine learning models, offering consistency and reproducibility across different computing environments.
By encapsulating models and dependencies, organizations can streamline deployment workflows and promote collaboration across teams. Additionally, container orchestration platforms like Kubernetes provide robust infrastructure for deploying and managing containerized machine learning applications at scale. Leveraging these platforms, data scientists can orchestrate complex deployment scenarios, automate scaling, and ensure high availability of deployed models.
5. ML Model Versioning
Versioning is one of those aspects that we tend to ignore until they become a problem. This is partly because versioning is rarely an issue when we talk about a handful of models but can become a total nightmare in a medium to large-scale ML infrastructure. Paradoxically, versioning is an element every software developer is familiar with as it’s the cornerstone of processes such as continuous integration or deployment, which rule the lifecycle of most modern software applications. However, version control in ML solutions takes a different connotation. What makes version control different in ML models is that we are not talking only about code versioning but also about data and trained model versioning.
ML model versioning involves systematically tracking changes to models over time, including modifications to code, data, hyperparameters, and training processes. Effective versioning allows data scientists to revisit and reproduce previous model iterations, facilitating experimentation and comparison of different approaches. By associating each model version with metadata such as training data, evaluation metrics, and deployment details, data science teams can ensure accountability and traceability throughout the model development lifecycle.
6. A/B Testing for ML Models
A/B testing is a well-established practice in modern software applications, but it’s not trivial when it comes to ML pipelines. After all, testing an ML model does not only involve testing the model itself but also the corresponding datasets and hyperparameters. From that perspective, A/B testing in ML models is relatively different and more complex than in traditional software applications. In the context of machine learning, it enables data scientists to assess the impact of model changes, feature modifications, or hyperparameter tuning on key performance indicators such as accuracy, conversion rates, or user engagement. By randomly assigning users or data points to different model variants, organizations can gather statistically significant insights into the relative effectiveness of each approach.
Moreover, A/B testing provides a rigorous framework for validating model improvements before rolling them out to production, mitigating the risk of deploying suboptimal solutions. It also facilitates continuous experimentation and iteration, allowing data scientists to refine models iteratively based on real-world feedback.
7. CI/CD in ML Solutions
CI/CD is a well-established concept in traditional software development. But in the world of ML, it is taking its first steps. Like in traditional software, CI/CD in ML focuses on streamlining the ML solutions’ delivery and management, but the specifics look quite different from established CI/CD concepts. Establishing a CI/CD pipeline for ML applications requires considering aspects such as model training and optimization, which have no equivalent in traditional software systems. At a high level, here are some of the components of ML CI/CD pipelines:
Continuous Integration: This phase includes stages such as model unit testing, training convergence validation, and integration between the different components of the solution.
Continuous Delivery: This phase includes infrastructure validation, model performance testing, retraining processes, model serving, and monitoring.
In CI/CD pipelines for ML solutions, automation plays a central role, facilitating seamless integration of code changes, feature updates, and model improvements into production environments. Automated testing frameworks validate model performance against predefined metrics, enabling data scientists to maintain high-quality standards. By embracing CI/CD practices, organizations can accelerate time-to-market for ML solutions, iterate more rapidly in response to changing requirements, and maximize the value derived from data-driven initiatives.
Simplify MLOps with UnifyAI
With UnifyAI, organizations today are seamlessly building the MLOps pipeline to experiment with AI models which includes training, deployment, managing and monitoring AI models. The UnifyAI core engine acts as a central orchestrator for the whole MLOps pipeline, which handles model deployment, model monitoring & real-time inference. It facilitates the following:
- An integrated development environment is provided to the data scientist/user to experiment with and train AI models.
- Data scientists/users can store the experiment results into model registry & choose the candidate model for registration along with versioning capability through metric comparison.
- One click model deployment from the UnifyAI user interface.
- It handles metadata required for inference for deployed models.
- A user-friendly user interface that handles inference requests for UnifyAI platform, including getting required data from the feature store.
- A user-friendly user interface to evaluate and monitor model performance.
Components of the UnifyAI MLOps pipeline:
1. UnifyAI IDE:
UnifyAI IDE provides an environment for data scientists/users to experiment with AI models and log those to the model registry. It helps in following way,
Features can be extracted from the Feature store which will be further used for training purposes.
The model will be tested against applicable metrics.
Models & their metrics will be pushed to the model registry.
2. UnifyAI Model Registry & Repository:
The main purpose of model registry is to store experiment results. Different hyper-parameters are tested on the specific models and the final chosen models are registered. Model related artifacts are stored in the model repository. It helps in the following ways:
Experiments, including different hyper-parameters logged during model experimentation, can be traced through its user interface.
Specific models can be registered in the model registry, along with the processing required for models with a version tag.
Integration with the UnifyAI core engine helps to enable automated model deployment.
3. UnifyAI Model Deployment:
Model Deployment facilitates a one click deployment facility for the registered models. UnifyAI Core Engine handles the task of automated model deployment as well as model inference. It helps in the following way,
Once the model is registered, using the UnifyAI user interface, scientists and users can deploy the model with just one click.
Deployed model supports single as well as batch inferences.
Apart from this, models are deployed as REST/GRPC microservice containers, which add auto scaling, load balancing, etc. functionalities to it.
4. UnifyAI Monitoring:
Monitoring provides a user-friendly interface for users to visualise the model performance in the real-time production environment. It helps in the following way,
Monitoring provides two main two types of metrics: Data Drift metrics and API-level metrics.
Data drift metrics help data scientists/users to identify the drift in data at the time of inference, while API metrics provide the API level performance of models such as error rate, latency etc.
Want to build your AI-enabled use case seamlessly and faster with UnifyAI?
Talk to us today.
Authored by Hardik Raja, Senior Data Scientist at Data Science Wizards (DSW), this article delves into the realm of MLOps (Machine Learning Operations), extending DevOps concepts and enabling managing the lifecycle of ML applications. It covers various aspects including version control, CI, monitoring, continual training, Configurability, scalable production deployment and how they are streamlined through UnifyAI; Using it, enterprises can accelerate the development of Machine learning solutions, paving the way for enhanced creativity, efficiency, and competitiveness.
About Data Science Wizards (DSW)
Data Science Wizards (DSW) is a pioneering AI innovation company that is revolutionizing industries with its cutting-edge UnifyAI platform. Our mission is to empower enterprises by enabling them to build their AI-powered value chain use cases and seamlessly transition from experimentation to production with trust and scale.
To learn more about DSW and our ground-breaking UnifyAI platform, visit our website at www.datasciencewizards.ai. Join us in shaping the future of AI and transforming industries through innovation, reliability, and scalability.