Machine Learning Operations (MLOps) is rapidly becoming a sought-after term in the business industry. ModelOps aims to bring together the development and deployment of machine learning systems into one concise process, enabling organisations to create and deliver high-performing models in production. By streamlining this process, companies can reap the benefits of a streamlined and efficient workflow.
This article will provide an overview of MLOps and how it functions. We will walk through each phase included in the MLOps pipeline, so that you can gain a clear understanding of the general process at the core of MLOps. Additionally, if you are new to the field of machine learning, you will gain an insight into how predictive models are created and deployed. By the end of this article, you should have a firm grasp of the fundamentals of MLOps.
What is the function of MLOps?
As we enter a new era of advanced technology, the amount of data that is being produced is unprecedented. According to current estimates, by 2025 there will be approximately 463 exabytes of data created each day around the globe. This exponential increase in data production has led to the necessity of reliable data storage and scalability of machine learning (ML) processes. In order to address these issues, MLOps is a useful tool. Despite the desire to automate as much as possible, only a small portion of models are currently being used for this purpose. This is often due to the time constraints and other technological difficulties. In response to this, MLOps is a valuable initiative that seeks to improve and streamline the process.
The Machine Learning Operations (MLOps) lifecycle encompasses a range of activities, including the generation of models, continuous integration and delivery, deployment, orchestration, governance, health and diagnostics monitoring, and the analysis of business metrics. Each of these activities is necessary in order to ensure successful implementation and maintenance of MLOps, as well as the optimisation of machine learning models.
Workflow in MLOps
When discussing the concept of a ‘workflow’, we are referring to the process and the sequence of steps required to complete a given task. In the context of MLOps, the workflow is focused on the development and implementation of reliable and robust machine learning solutions.
In MLOps, the process is often separated into a “top” (the pipeline) and “bottom” (the tasks) (driver). These layers consist of the following parts:
The driver might be anything from data to code to artefacts to middleware and infrastructure. This is where the pipeline comes in.
Please refer to the accompanying illustration for clarification.
Drivers under the ground make the pipeline possible. This pipeline facilitates rapid ML model prototyping, testing, and validation.
Each component is described in further depth, with examples of its use, below.
Water transport system
The pipeline, as previously mentioned, is the top level. Models may be deployed and tracked with its help.
Create a new module
The ML models are trained and given a version using this component. The first stage, shown in the following figure, is “data ingestion.”
Example of use: Let’s say it’s necessary to implement an image processing service in a highway CCTV camera.
Processing of incoming data (Data Ops)
The implementation of a Machine Learning Operations (MLOps) life cycle starts with the establishment of a data intake pipeline, which is responsible for gathering data from a variety of sources, such as a data lake or data warehouse. A data lake is a repository of large amounts of data, both structured and unstructured, which can be used to store and manage data in a single centralised location. Once the data intake pipeline is established, it will allow for the collection of data from these various sources, making it easier to access and manipulate the data.
Data intake is the first step in the process, which is followed by validation logic-enabled data verification and validation. These processes are carried out through an Extract, Transform, Load (ETL) pipeline that is connected to the relevant data sources. Once the data has been collected, it is divided into a training set and a test set. This allows us to train and evaluate our models on a reliable and accurate dataset.
Example of use: In this situation, the data will be represented by a large quantity of images of cars on and off the roads. We will create two separate datasets, one for training the model and one to evaluate its accuracy.
Training of Models
As the project draws to a close, it is time to teach the machine learning model to act preemptively. This will involve running modular programs to perform the standard model training tasks such as data preparation, cleansing, and feature engineering. It may be necessary to adjust some hyperparameters manually, however, it is recommended to utilise an automated system, such as a grid search, to find an optimal solution.
After this is done, you will have a trained model.
Example of use: This study aims to develop a Machine Learning (ML) model to accurately classify cars into various categories by utilising a Convolutional Neural Network (CNN). Through training, a CNN model is created that is capable of accurately categorising automobiles into different classes.
Evaluation of a trained model
Once the model has been trained and deployed, its effectiveness can be gauged by its accuracy in predicting the results of new data. The model’s performance should be evaluated based on the range of values derived from the metric score.
Example of use: Both the test data and the training data have been classified, so it is now time to put the models built from the training data to the test by evaluating them on the test data to determine their performance. Precision scores are a useful metric for measuring the quality of the model, and the training process is completed when the results of the trained model are satisfactory.
Once testing has been completed, the next step is to implement Docker encapsulation, which will enable the program to run without any external assistance. Docker is an effective technology as it combines the code, operating system, libraries, and other dependencies necessary for the program to execute into separate, deployable components of the application.
Example of use: In this paper, we present a convolutional neural network model designed for the purpose of categorising highway vehicles. This model is now prepared to proceed to the subsequent stages of the process.
Registration of Models
After completing the prior step of containerizing the model, it is necessary to register the model in the model registry. Registration of a model involves the filing of the necessary files which are used to construct, represent, and execute the model.
As the whole ML pipeline has been run, the model has been trained and is now ready to be deployed to the production environment.
Example of use: The model registration is complete. The vehicle categorization using a security camera – project may immediately do this.
The two core components of the MLOps process are the pipeline and the driver. In order to fully deploy an ML application, a number of steps must be completed, including data ingestion, model training, and model testing. Data ingestion is the process of transforming the data set to make it suitable for training a model. Model training is the process of training the ML model using the training dataset. Lastly, model testing is the process of testing the performance of the trained model on the test dataset and obtaining performance scores.
The Build module enables us to create Machine Learning models which can then be deployed with the assistance of the Deploy module. In this module, we have the opportunity to test our model in a real-world environment to ensure that it is strong and flexible enough to be implemented on a broader scale.
A visual representation of the deploy process is shown above. The two primary parts are the testing phase and the release phase.
Prior to deploying an ML model in a live system, it must be thoroughly evaluated to guarantee its sustainability and efficiency. To attain this, the trained models are initially tested in a simulated version of the live system to guarantee they can operate well under real-world conditions. Subsequently, the models are deployed in the test environment (pre-production) to examine their performance.
Based on the established requirements and use cases, machine learning models can be deployed as an Application Programming Interface (API) or streaming service to various deployment targets in the test environment. These deployment targets may include Kubernetes clusters, container instances, scalable virtual machines, and edge devices. After the model is deployed, it can be used to make predictions based on the test data. At this stage, we execute inference on the model in bulk or at regular intervals in order to evaluate its stability and efficiency.
The performance of the model is next evaluated, and if it meets criteria, it is sent on to the manufacturing phase.
The models that have been tried and tested are being put to use in the manufacturing phase.
Example of use: The model acts as an interface between a roadside CCTV system and a remote user. By connecting to the CCTV system, the model receives visual footage of cars travelling on the road and uses this data to make inferences about the types of vehicles present at any given time.
Functions as a monitor
Both the monitoring and the deployment components of the ML application are closely linked. The ML application is deployed and then monitored and analysed during the monitoring phase to measure its success. For example, one can compare the expected outcomes of the model with the actual outcomes obtained from the real car.
A predetermined explainability framework is also used to assess the model. Quality control included into the model allows for actionable regulation.
This component monitors the effectiveness of the application, the accuracy of the models used to produce results, and the security of the data. In order to assess the application’s performance, telemetry data can be collected and analysed. This graph illustrates how the performance of the devices in a production system has changed over time. Telemetry data from accelerometers, gyroscopes, humidity, magnetometers, pressure, and temperature can be used to track the performance, health, and durability of the production system.
Monitoring the performance of Machine Learning (ML) models in production is critical for ensuring that they are doing their job optimally and that any business decisions or impacts resulting from them are done in a legally compliant manner. To further increase their commercial value, explainability techniques are applied in real-time to assess the key components of the model including fairness, trustworthiness, bias, transparency, and accuracy. This helps to ensure that the model’s outputs are reliable and trustworthy.
By monitoring and analysing the performance data of the installed program, we are able to ensure that it is functioning optimally, and delivering the desired outcomes for the business or machine learning (ML) process. By regularly keeping track of performance, we can generate any necessary warnings or take corrective action in order to maintain the system’s efficiency.
When model performance drops below an established threshold, the Product Owner or Quality Assurance Expert is notified so that they can take action. The process then requires them to initiate a new model training and deploy it in place of the previous one.
Governments must ensure that their policies comply with all applicable national and international regulations. It is of utmost importance that the models used to create the policies are easily understandable and traceable. To ensure this, it is beneficial for governments to audit and report on their production models to ensure transparency and clarity.
In this instance, the efficacy of the model is monitored and assessed at the implementation site (e.g., a computer-connected surveillance camera). Should the model’s precision score for vehicle classification be below 40%, an alert is activated, prompting the model to be retrained in order to increase its resilience and accuracy.
The MLOps process comprises two principal components: the pipeline at the top and the driver at the bottom. Data ingestion involves all the necessary modifications to the dataset before training the ML model to use it; model training is the process of training the ML model using the training dataset; model testing is the process of evaluating the performance of the trained ML model using the test dataset; and finally, release, monitoring, and analysing are all part of the MLOps process.