MLOps, short for machine learning operations, is a novel concept of Artificial Intelligence (AI). Its inception can be traced back to 2015, originating from a paper titled “Hidden Technical Debt in Machine Learning Systems.” Since then, its growth has been notably robust, and the expected market worth of MLOps solutions is projected to reach $4 billion by 2025.
Nevertheless, Santiago Giraldo, the Senior Product Marketing Manager and Data Engineer at Cloudera, once shared, “Putting ML models in production, operating models, and scaling use cases has been challenging for companies due to technology sprawl and siloing. In fact, 87% of projects don’t get past the experiment phase and, therefore, never make it into production”.
So, with MLOps challenges as significant as these, how to implement MLOps successfully?
In this article, we will delve into the essential aspects of implementing MLOps effectively, addressing three core methods and detailed steps involved.
Let’s dive right in!
What Is MLOps?
MLOps aka Machine Learning Operations, encompasses practices that effectively manage, maintain and construct machine learning systems. It focuses on the seamless transition of models from conception to production, ensuring agility and cost-efficiency while overseeing goal alignment.
CDFoundation asserts MLOps as an extension of DevOps, extending inclusion to machine learning and data science within DevOps practices.
MLOps highlights automation, traceability, reproducibility, and quality assurance within machine learning pipelines and model development. These MLOps pipelines span various stages of the machine learning lifecycle.
It’s important to note that MLOps methodologies can be selectively applied to specific phases of the machine learning process, such as:
- Data extraction and storage
- Data labeling
- Data validation and data cleaning
- Data and code versioning
- Exploratory Data Analysis (EDA)
- Data preprocessing and feature engineering
- Model training and experiment tracking
- Model evaluation
- Model validation
- Model serving
- Model monitoring
- Automated model retraining
How To Implement MLOps
There are three ways you can take into consideration when implementing machine learning operations, including MLOps level 0 (manual process), MLOps level 1 (ML pipeline automation), and MLOps level 2 (CI/CD pipeline automation).
1. MLOps Level 0 – Manual ML Operation Implement
This is the standard for companies venturing into machine learning for the first time. If your models don’t have to undergo many changes or training, a wholly manual machine learning workflow driven by data scientists may be good enough.
- Manual, script-driven, interactive procedure: Each step is executed by hand, from data analysis to model training and validation. It demands manual execution and transition between stages. Typically, a data scientist iteratively writes and executes experimental code in notebooks until achieving a workable model.
- Disconnection between ML and operations: This process separates model creators (data scientists) from engineers responsible for deploying machine learning models as prediction services. Data scientists hand over a trained model as an artifact for engineering teams to deploy on their API infrastructure. This handover can involve placing the model in storage, recording it in a code repository, or uploading it to a model registry. Subsequently, engineers handling model deployment must make necessary features accessible for low-latency serving, potentially leading to training-serving disparities.
- Infrequent release versions: This process assumes that your data science team handles a small number of models that undergo infrequent changes, be it altering model implementation or retraining with new data. A new model version is typically deployed only a few times per year.
- No Continuous Integration (CI): Due to several expected implementation changes, CI is not applied. Code testing usually occurs as part of notebook or script execution. The scripts and notebooks used to execute experimental steps are under source control, producing artifacts like trained models, evaluation metrics, and visualizations.
- Lack of Continuous Deployment (CD): As ML model version deployments are infrequent, CD is ignored.
- Deployment primarily for prediction services: This process focuses on deploying the trained model solely as a prediction service, for instance, as a microservice with a REST API rather than the entire ML system.
- Inadequate active performance monitoring: This procedure does not actively track or log model predictions and actions, which is essential for identifying ML model performance degradation and other behavioral shifts.
Software engineers might have their intricate setup for API configuration, testing, and deployment, which may include security, regression, and load and canary testing. Additionally, when introducing a new version of an ML model into production, it typically involves A/B testing or online experiments before becoming the primary prediction request handler.
Machine learning models frequently encounter issues when deployed in real-world settings. They struggle to adapt to changing environmental dynamics and data alterations.
To tackle these challenges inherent in the manual process, leverage MLOps practices for Continuous Integration/Continuous Deployment (CI/CD) and Continuous Training (CT). The incorporation of an ML training pipeline empowers CT while establishing a CI/CD system to expedite the testing, development, and deployment process of fresh ML pipeline iterations.
2. MLOps Level 1 – ML Pipeline Automation
MLOps Level 1 aims to attain continuous training (CT) of the ML model through the automation of the machine learning pipeline. This approach allows for the model’s continuous delivery as prediction services.
MLOps Level 1 method proves valuable for machine learning solutions. It navigates ever-evolving landscapes and necessitates a proactive response to shifting customer behaviors, fluctuating pricing, and other dynamic indicators.
- Swift experimentation: The steps within the ML experiment are meticulously coordinated. Automation facilitates seamless transitions between these steps, enabling rapid iterations and better preparedness for moving the entire pipeline into production.
- Production-Based CT: The ML model undergoes automatic training in a production environment, utilizing real-time pipeline triggers.
- Experimental-Operational Harmony: The pipeline implementation used in both the development/experimentation phase and the preproduction/production phase forms a crucial aspect of MLOps, uniting DevOps practices.
- Modularized Component and Pipeline Code: To construct ML pipelines, components must be reusable, composable, and potentially shareable across various ML pipelines, often through containerization.
- Continuous Model Delivery: In a production setting, an ML pipeline provides prediction services for new models trained on fresh data. The model deployment step, serving the trained and validated model as an online prediction service, is fully automated.
- Pipeline Deployment: In Level 0, you deploy a trained model as a prediction service for production. In Level 1, you deploy an entire ML training pipeline, which operates automatically and repeatedly to serve the trained model as the prediction service.
- Data and model validation: In the production pipeline, automated data and model validation steps are indispensable as the pipeline relies on fresh, real-time data to generate new versions of ML models.
- Feature store: A feature store acts as a centralized repository, standardizing feature definition, storage, and access for both model training and service.
- Metadata management: Recording information on each ML pipeline execution aids in tracking data and artifact lineage, ensuring reproducibility, and facilitating comparisons. It also serves to debug errors and anomalies.
- ML pipeline triggers: It’s possible to automate ML production pipelines to retrain ML models with new data based on your specific needs: on demand, on a schedule, on the availability of new training data, on model performance deterioration, or significant shifts in data distribution, such as evolving data profiles.
ML pipeline automation is most suitable when deploying new machine learning models based on fresh data rather than novel ML concepts. Nonetheless, new ML ideas and the swift deployment of fresh ML component implementations are crucial. Organizations managing numerous ML pipelines in production should consider a CI/CD setup to automate the construction, testing, and deployment of these pipelines.
3. MLOps Level 2 (CI/CD Pipeline Automation)
A robust automated CI/CD system ensures the rapid and dependable enhancement of production pipelines. This automated CI/CD system empowers data scientists to swiftly explore fresh concepts related to feature engineering, model structure, and hyperparameters.
This level best serves technology-driven enterprises that require daily if not hourly, model retraining, minutes-long updates, and simultaneous redeployment on thousands of servers.
The MLOps configuration encompasses several vital components:
- Source control
- Test and build services
- Deployment services
- Model registry
- Feature store
- ML metadata store
- ML pipeline orchestrator
- Development and experimentation: Iterative experimentation with new ML algorithms and modeling, orchestrated steps drive this phase. It yields source code for ML pipeline steps, subsequently stored in a source repository.
- Continual integration of pipelines: This phase performs source code building and multiple tests. Outputs consist of packages, executables, and artifacts for later deployment.
- Pipeline continuous delivery: Artifacts generated in the continuous integration stage are deployed to the target environment in this step, resulting in a deployed pipeline with the new ML model implementation.
- Automated Triggering: The pipeline is automatically activated in the production environment following a predefined schedule or in response to a trigger. This stage culminates in a freshly trained model added to the model registry.
- Continuous model deployment: Trained models are employed as prediction services in this phase, leading to the deployment of model prediction services.
- Monitoring: Collecting ML model performance statistics based on live data is an integral component when monitoring. It triggers the pipeline execution or instigates new experimentation cycles.
The data analysis step still relies on manual processes by data scientists before initiating a new iteration of the experiment. Similarly, ML model analysis remains a manual undertaking.
5 Essential Steps of Successful MLOps Implementation
When implementing MLOps, there are a few vital steps you need to bear in mind for successful execution, from preparing data to retraining the model.
Step 1: Data Preparation and Ingestion
This is the first but the most initial step in managing machine learning. Data scientists need to collect, extract, and store data, as well as clean and transform it into a structured format. The procedure helps organizations possess high-quality data suitable for model training, feature engineering, and analysis.
MLOps involves the establishment and control of data pipelines too, alongside the automation of data ingestion processes, ensuring data quality, consistency, and reliability for utilization.
Step 2: Machine Learning Model Development
Development is the process of constructing, training, versioning and assessing machine learning models. This covers feature engineering, ML model selection, and hyperparameter optimization tasks.
Model development represents a pivotal facet of MLOps, as it empowers machine learning teams to create pipelines that guarantee ML model accuracy, dependability, scalability, and swift deployment to production. These operations are all completed automatedly.
Step 3: Machine Learning Deploying in Production
The deployment phase centers on making the machine learning model accessible in real-world scenarios. The ML team will package the model and serve it to an inference server or framework to manage real-time requests and ensure scalability and load balancing. MLOps’ CI/CD practices facilitate the automation of this process, leading to expedited and more reliable deployment cycles.
Step 4: Continuous Monitoring and Management
Gradually, ML models might experience deviations that lower their capacity to fulfill business requirements. Consequently, post-deployment models necessitate ongoing monitoring to validate their accuracy, dependability, availability, and performance.
This includes the continual tracking of metrics like response times, throughput, error rates, and resource utilization. Such metrics are monitored without interruption as part of MLOps to promptly detect performance deterioration and initiate model retraining to resolve issues.
Step 5: Retraining
As data evolves, ML models may require retraining to sustain their performance. Retraining enables the model to adapt and potentially improve its performance and precision.
Retraining encompasses new data collection and preprocessing, model updates, training with fresh data, evaluation, and deployment. This process can be carried out automatically through a retraining machine learning pipeline.
There are 3 key methods that you can take when it comes to MLOps implementation. They include MLOps level 0 for starters, MLOps level 1 for continuous training, and MLOps level 2 for CI/CD automation. You should rely on your purposes along with their characteristics and challenges to choose the right one for your organization.
For a successful MLOps implementation process, carry out the five essential steps: data preparation and ingestion, model development, deploying in production, continuous model monitoring and management, and retraining. These steps establish adaptable, reliable, and continuously optimized machine learning models.
Ready to take your MLOps journey to the next level? Contact us now, Neurond’s MLOps Service is here to help you navigate the complexities of MLOps and achieve great success.