MLOps Architecture: Building your MLOps Pipeline
Learn about the challenges of setting up an ML production pipeline and gain helpful guidance from this blog post. Discover why only 20% of pipelines deliver business value and start building your own successful pipeline today.
Machine Learning Operations (MLOps) is a relatively new field that has emerged as a solution to the unique challenges faced by machine learning (ML) teams. MLOps aims to improve the efficiency and effectiveness of ML teams by automating and streamlining their workflows, from data collection to model deployment.
One of the key components of any MLOps framework is the MLOps pipeline, which is responsible for managing the flow of data and code through the ML development process.
Industries that benefit from MLOps -
Already, various industries have felt the impactful benefits of MLOps tools in their own operations. Some sectors that have seen the advantages of integrating this process include:
- Finance - Machine learning serves numerous purposes for businesses in the finance sector, including loan approvals, creditworthiness, financial planning, credit fraud detection and many more. By learning the user’s behaviors over time, the ML algorithm can take a more fine-tuned approach when determining normal versus abnormal actions within that user’s profile.
- Travel - Travel companies can use MLOps to create customer recommendations and personalizing travel packages. Machine learning can help personalize experiences and help a travel business adopt automated processes like call handling and service pricing.
- Transportation - Machine learning techniques can be used to predict and potentially avoid flight delays for airlines.In turn, fewer delays can create a better experience for travelers, especially when you provide individuals with access to real-time information about their flights.
- Food ordering and delivering - Businesses in the food industry like Dominos and DoorDash employ ML models for tasks like estimating delivery times, recommending restaurants or specific merchant deals to consumers, offering dynamic pricing and creating efficient routes for delivery drivers. Using ML models in the food delivery sector can be crucial for properly allocating resources and workers as needed, saving companies more money and helping them preserve more resources.
What is the Goal of an MLOps Pipeline?
The goal of an MLOps pipeline is to efficiently apply a machine learning model to incoming data at scale while minimizing operational costs.
Both the data scientist and the ML engineer have challenging jobs. The data scientist is working with the unknown and has yet to guarantee that the data will provide valuable insights to address the needs of the business. The data may be too noisy, not contain the appropriate features, or even need to be more. The challenge for the data scientist is to find the answer when the answer may not exist.
The ML engineer also has a challenging job. However, assuming the data scientist successfully provides a model, the ML engineer (with enough time and resources) is almost guaranteed success. The data scientist has already proven that a solution exists. It is now the responsibility of the ML engineer to apply the model automatically to new data at scale. An MLOps pipeline is intended to reduce the effort ML engineers spend operationalizing each new model by providing standard utilities for deployment.
Let’s explore the architecture of an MLOps pipeline and how to build one that is efficient, scalable, and easy to maintain.
Step 1: Data Ingestion
The first step in building an MLOps pipeline is to ingest data from various sources. This step involves collecting data from multiple sources, cleaning and transforming it, and preparing it for analysis. Typically, data ingestion involves using a variety of data sources, such as databases, APIs, and flat files. It is essential to ensure that the data being ingested is of high quality, consistent, and relevant to the ML problem at hand.
Step 2: Data Preparation
After the data has been ingested, the next step is to prepare it for analysis. This involves cleaning and transforming the data, including removing null values, handling missing data, and converting data types. Data preparation is critical as it can significantly impact the accuracy of the ML model. Therefore, it is important to ensure that the data preparation process is accurate, reliable, and scalable.
Step 3: Model Training
Once the data has been prepared, the next step is to train the ML model. Model training involves using the prepared data to train an ML model, which is then evaluated for accuracy. Model training can take a long time, depending on the complexity of the model and the size of the data set. Therefore, it is essential to use scalable infrastructure that can handle large volumes of data and can be easily scaled up or down as required.
Step 4: Model Evaluation
After the model has been trained, it is next to evaluate its accuracy. Model evaluation involves comparing the predictions of the model to the actual values in the test data set. This step is critical as it helps to identify any issues with the model, such as overfitting or underfitting. It is essential to use a reliable evaluation framework to ensure that the model is accurately evaluated.
Step 5: Model Deployment
Once the model has been trained and evaluated, the next step is to deploy it. Model deployment involves taking the model and making it available for use in a production environment. This step can be challenging as it involves integrating the model with other systems and ensuring that it is highly available and scalable.
Step 6: Model Monitoring
The final step in the MLOps pipeline is model monitoring. Model monitoring involves tracking the performance of the deployed model and identifying any issues that may arise. This step is critical as it helps to ensure that the model is performing as expected and that any issues are identified and resolved quickly.
Other Considerations for Building an MLOps Pipeline
1. Establishing Version Control —
Version control is an essential component of any MLOps pipeline. It enables you to track changes to the code, data, and models, and revert to previous versions if necessary.
As a first step, you should use a Continuous Integration/Continuous Deployment framework for your ML pipeline. This allows you to work quickly in small iterative cycles on an ML pipeline that is always working. Focus on putting all your source code under version control (i.e. GIT, Visual Studio Team Services, Team Foundation Version Control). As your pipeline grows, you will have source code for:
- Cleaning and preparing the raw data
- Deploying the model
- Applying the model
- Gathering, storing, and reporting the results
- Testing the model with unit tests, integration tests, and regression tests
- And the list goes on and on
Keeping track of this code without version control software will be a difficult and time-consuming task (especially as your codebase and team grow).
In addition to source code, models will also have to be versioned. This can be achieved with a model registry. We recommend adopting a minimalist model registry such as MLflow over developing custom model-storage architectures. Even if storing models in an object store like S3 could technically pass for versioning, a registry will do much more to streamline model development and handoff.
2. Automated Testing - Implementing a CI/CD Pipeline
Testing is critical to ensure that the ML model is accurate and reliable. Automated testing can reduce the time and effort required to ensure the model is thoroughly tested.
Once your software is under version control, add on continuous integration by incorporating automated testing into your CI/CD pipeline. Each time you check in your code, you will want to verify that each test passes. This is critical for developing reliable software and ML pipelines.
As your ML pipeline matures, you will quickly become tired of the manual process of building, testing, and deploying your models. Building, testing, and deploying your ML pipeline will lead to errors and frustration. This is especially true if you work in small iterative cycles. The sooner you can automate these processes, the faster you will be able to minimize errors and focus on higher-value work. Depending upon your development platform, you may use Jenkins, GitHub Actions, GitLab CI/CD, AWS CodeBuild, or Azure DevOps for your CI/CD. Whatever tools you decide to use for your CI/CD, keep it as simple as possible.
The following picture shows the automated ML pipeline with CI/CD routines:
3. Improve Clarity and Reliability - Implement Logging
After you have implemented a robust CI/CD pipeline, we recommend focusing on adding logging to your ML pipeline. As discussed in the tracking section, logging is an essential part of any ML pipeline and will help you achieve clarity and reliability. We will categorize logging into two categories: external and internal.
- External logging keeps track of what model is applied to which data.
- Internal logging will allow you to see the inner workings of your ML pipeline and debug problems quickly.
In the beginning, your ML Pipeline will be operating in short iterative cycles. As you are iterating on the ML pipeline, the data scientist may also be experimenting with different models to improve performance. With external logging, it will be easier to track which models were applied to which data and what the results were.
Finding the best model that provides the most business value will be easier if not impossible with meticulously logging. When implementing logging, make sure your logs can be traced in some way to the model and software versions tracked in your model registry and version control. The data that is applied to the model along with the results should also be logged. This will allow the data scientist to keep track of the model’s performance over time. An additional benefit is that these logs can be used to detect data drift.
As the ML pipeline matures, keeping track of this metadata may be challenging. Containerizing the code and model is one way to simplify the logging of dependencies. Creating a Docker container with the code, code dependencies, and model provides a convenient way to package the core components of the ML pipeline.
Monitoring your ML pipeline will be critical for extracting business value. You will want to monitor performance metrics such as uptime, calculation time, and latency. You should also monitor how often the ML pipeline delivers actionable insights. An ML pipeline only delivers business value if someone acts upon the results. When you monitor your ML pipeline, keep it simple. Focus on performance and operational metrics.
Performance Metrics will help you measure the business value of the ML model. For example, a predictive model only provides value if its predictions are accurate. Comparing model predictions to actual outcomes will allow you to verify that the model functions appropriately and provides business value. Defining good performance metrics is exceptionally challenging. Stay encouraged if it takes you several iterations before you find the right set of performance metrics.
Operational Metrics will help you with the daily operation of your ML pipeline. Operational metrics are easier to define than performance metrics. You can measure latency, throughput, or the rate at which the model is called. These operational metrics will allow you to monitor the performance of your pipeline over time. If your performance metrics deviate from historical norms, this could be an early warning sign that your pipeline requires additional attention.
For example, maybe your model’s throughput has reached a plateau, and many of your predictions are failing. This could indicate that your current computational resources are insufficient to keep up with demand. Increasing your computational processing power and memory could be a simple solution to return your pipeline to operating norms.
In addition to monitoring performance and operational metrics, you will also need to monitor for drift. We live in a dynamic and constantly changing world. The assumptions and conditions used to train the model will eventually depart from reality. Model performance will degrade over time. This is known as model drift. You can monitor for model drift by careful monitoring of your performance metrics.
Whenever possible, leverage tools and frameworks that reduce the overhead of monitoring metrics in real-time. AWS CloudWatch provides tools for dashboarding performance metrics of your applications. Third-party tools like Grafana and Streamlit can also relieve the burden of reporting metrics without becoming locked into a cloud provider.
An ML pipeline that focuses on simplicity, version control, logging, active performance monitoring, and has an established CI/CD process, is a great start!
You are well on your way to extracting business value from your data with ML pipelines. As you become more experienced with building ML pipelines, you will find opportunities to improve them. You will also discover and build better tools to monitor your pipeline’s performance and operational metrics. Don’t be afraid to experiment with ways of improving your pipeline. Let your pipeline evolve to meet your needs while always focusing on simplicity.
When Should I Start Building my MLOps Pipeline?
We draw a distinct difference between model development and experimentation by the data scientist and the design and implementation of the MLOps production pipeline by the ML engineer. Ideally, the data scientist and ML engineer will work together in small iterative cycles to produce the best results. How and when they work together will depend entirely on the needs of the business and customer.
In the early stages of development, the data scientist explores the data by becoming familiar with it and searching for actionable insights. At this stage, it may be too early to start building the MLOps production pipeline for the model. With no guarantee that the data scientist will be successful, any effort to build an MLOps production pipeline may be well-spent. However, the business may decide that an MLOps production should be developed parallel to minimize risk and meet deadlines.
In either case, building the model and the MLOps production pipeline are two different processes that should be loosely coupled. We consider this loose coupling as a best agile practice. It allows the data scientist to iterate and improve upon the model while allowing the MLOps engineer to independently refine, scale, and improve the MLOps Pipeline. This loose coupling doesn’t mean that the data scientist and ML engineer should work independently.
For a successful MLOps pipeline, the data scientist and ML engineer should have constant and direct communication. They should work together to understand the components of the model, the format of the model, the model inputs, and the model outputs. If the data scientist and ML engineer can decide on standard interfaces, then the model can be easily updated in the ML pipeline.
Model registries are a common way for data scientists and ML engineers to manage models collectively. Agreeing to use a model registry early in the development cycle will standardize the transferring of models from data science to engineering. Model registries also benefit data scientists in the form of automatic logging so that more experiments can be performed with less bookkeeping overhead. Essential registry tools are lightweight and don’t require sophisticated infrastructure; the small cost of adopting the tool will pay dividends as your models evolve. MLFlow is an excellent open-source tool that includes a model registry.
Deciding on these requirements and development tools early will allow the ML engineer to build the infrastructure of the ML Ops pipeline. Design decisions for the MLOps pipeline should be flexible enough to work for multiple projects. This will allow components of the MLOps pipeline to be standardized and reused.
We hope this blog post has provided you with some helpful guidance on how to begin setting up an ML production pipeline. It isn't easy to create an ML production pipeline that provides business value. If it were easy, more than just 20 percent of the pipelines would be delivering business value.
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Just a select few people, teams, or even huge companies have the variety of skills, expertise, and knowledge needed to build a successful ML pipeline. Always keep things straightforward. If your initial ML pipeline falls short of your business goals, keep going.
In conclusion, building an MLOps pipeline is a complex process that requires careful planning and design. By following the steps outlined in this article and taking into account the considerations above, you can build a robust and efficient MLOps pipeline that will help you to develop and deploy ML models quickly and effectively. With the right architecture, your MLOps pipeline can help your team to achieve better results, faster, and with less effort.