Machine learning is increasingly becoming an integral part of the modern digital landscape, with models being deployed in more industries and organisations than ever before. An exponential increase in the amount of data being collected by organisations has increased the complexity of processing this data. But the potential value an organisation can extract from ever-more granular data has also improved. Decisions can be made based on complex market forecasts, or trends from granular customer data.
Machine learning is at the forefront of this need for actionable insights from data. Machine learning techniques are being leveraged to automate data collection and analysis, and to predict complex trends and patterns. Whether in a product recommendation system or an intelligent email filtering system, machine learning techniques are being utilised across many different domains.
As machine learning embeds within the existing infrastructure of organisations, a range of challenges must be met to achieve effective integration. This includes questions about scaling and monitoring model performance, as well as understanding the dataflow within the organisation. In addition, the successful deployment of a machine learning model depends on different teams with different specialisms and skills. MLOps is the blending of these specialisms, combining data science, data engineering, and more traditional DevOps techniques. The aim is an understanding of both the model and the organisation’s infrastructure it sits within.
This guide explores the concept of MLOps, what it’s used for, and the different steps in the MLOps lifecycle.
What is MLOps?
MLOps is an approach to managing the entire machine learning lifecycle, from the development of a model to deployment and ongoing monitoring. The successful deployment and ongoing maintenance of a machine learning model is a complex task, and will require collaboration between different specialists within an organisation. MLOps is the combination of best practices from these different specialisms, resulting in a holistic approach to the model lifecycle. MLOps is therefore an emerging blend of best practice in machine learning, data science, data engineering, and IT development. The aim is to keep the complex elements which support a deployed model in sync.
MLOps takes many best practice approaches from the field of DevOps, which deals with development and IT operations. The aim of DevOps is to streamline this development process so that high-quality software can be delivered efficiently. DevOps brings together the best practises for software development and engineering, quality assurance, and IT operations. In addition, DevOps takes many elements and methods from the agile approach to software development, a series of practices for the entire development process. This includes development practices for design, coding, quality testing, and risk management.
However, MLOps will have many unique steps in its lifecycle compared to DevOps, because of the needs and requirements of machine learning. The tuning of hyperparameters in the training process, the selection of the type of machine learning approach, and the monitoring for ongoing model accuracy are all considerations in MLOps.
What does MLOps stand for?
MLOps stands for Machine Learning Operations, which refers to every stage in the machine learning lifecycle. It is a term for the techniques and approaches to developing, deploying and monitoring machine learning models. MLOps is an emerging field, with interest growing over the last few years as machine learning has become more commonplace in different organisations.
MLOps will combine the best practice approaches within machine learning and data science, data engineering, and DevOps. Machine learning operations can be understood as the whole model lifecycle, including how a model functions in the context of an organisation’s IT infrastructure. MLOps takes many of the best approaches from the more established field of DevOps, which covers the lifecycle of system development.
What is the MLOps life cycle?
MLOps is made up of a series of steps or elements which mirror the entire machine learning lifecycle. The sequence spans the initial data exploration and preparation, the training and tuning of the model, to the deployment and ongoing maintenance. The MLOps life cycle generally has three main phases: training, deployment and ongoing monitoring.
The training phase includes initial data preparation, experimentation, and model optimisation. The MLOps deployment phase will include steps to manage the model rollout, including model validation and testing, and system architecture. The final phase should include the ongoing monitoring of the model beyond deployment. Any drop in model accuracy or presence of outliers should be detected, and the cause should be established so the model can be realigned or retrained.
As with any successful machine learning deployment, the MLOps process has elements that are cyclical. Once deployed, a model should be continuously monitored for machine learning drift so that models can be retrained or tweaked. Models naturally become less effective over time for a variety of reasons if not retrained. One of the main causes is the context of the training data shifting from the real-world data the model is interacting with.
The MLOps life cycle may range in complexity between different organisations and their needs. However, it will follow a similar range of steps. The MLOps life cycle includes the following steps:
- Initial data collection and preparation
- Training and evaluating the model
- Establishing model governance processes
- Deploying the model to a live environment
- Monitoring and continuously optimising the model
Initial data collection and preparation
Machine learning requires a large array of training data to develop an accurate and efficient model. The first step will be to source a large enough array of quality data. Usually, a data scientist will perform exploratory data analysis to understand the data’s basic features or groupings. In the case of supervised machine learning, this data will need to be labelled and cleaned by a data scientist. In the case of unsupervised machine learning, the data still needs to be sourced and explored.
The sourced data will need to be prepared for the training phase, and this includes splitting the data into training and testing sets. This early phase of the MLOps life cycle is different from the direct model management at a later stage, but is still vital to the success of the whole project. The accuracy of the model depends on the validity of its training data.
Steps within this early stage of the MLOps life cycle include:
- Securing a reliable source of data at the right quantities, qualities, and format.
- Performing initial exploratory data analysis to understand the basic features of the data.
- Cleaning the data and labelling it in the case of supervised machine learning.
- Evaluating the data to detect bias training data.
- Splitting the prepared data into training and testing datasets.
Training and evaluating the model
Machine learning models are usually trained in a local or offline environment. The process will differ depending on the type of machine learning model being deployed, and the problem it is being developed to solve. For example, the training process will be different for a model built to cluster customers into similar user categories, compared to a model developed to predict fluctuations in the housing market.
Evaluation of the model is performed with cross validation techniques, which measures the model’s ability to process new data. As models learn from training data, there is a risk of overfitting to this data. Overfitted models may perform with a high accuracy when processing training data, but will perform poorly with new and unseen data. The evaluation phase will test the model on different subsamples of the training and testing data, establishing the degree to which the model is overfit.
Model governance processes
The MLOps life cycle will take into account considerations like model governance and any related regulatory compliance. MLOps focuses on establishing strong risk assessment and model governance processes, establishing the model as a tool within a wider organisation. This is particularly important within regulated industries where machine learning models are being leveraged to automate decisions. It can be difficult to explain a model’s decision-making because of the nature of the machine learning process, in which a system learns from the data itself. The explainability of black box models in particular is difficult to establish.
MLOps will ensure model governance is established as a key part of the process, and the model risks are clearly understood. This fits the machine learning model into wider conversations on risk management within the organisation. It also frames the model as a tool to achieve wider business objectives. There are a range of MLOps software options available to help track the MLOps process.
Deploying the model to a live environment
The deployment of a machine learning model requires the specialist knowledge of teams beyond the initial data scientists that may have developed the model. Deployment is where data scientists and DevOps specialists will overlap. Data engineers and IT specialists will be required to properly contextualise the model within the organisation’s system architecture, ensuring a dependable data flow and allocation of resources.
The model’s deployment environment will also need to be established, whether that’s on a server, the cloud, or through containerised deployment. MLOps stands as a common reference point to these different teams and stakeholders.
Monitoring and continuously optimising the model
Machine learning models and training data need to be continuously monitored and optimised to stay accurate in a live environment. Concepts of Continuous Integration and Continuous Deployment (CICD) from DevOps are used in MLOps to monitor and optimise the model. Continuous integration in this sense is the continuous testing of models and data to ensure ongoing validity. Continuous Deployment ensures model version control and rollouts.
Machine learning monitoring may cover the ongoing accuracy of the model, the detection of anomalies, or the auditing of decision logs. Data scientists will tweak the model’s hyperparameters or retrain the model entirely through a process of model optimisation. The model will also be monitored like more traditional software for use of GPU resources or user access. Data engineers or IT specialists will cover the allocation of resources or monitoring or data pipelines.
What is MLOps used for?
MLOps is used for the efficient development and deployment of a machine learning model. These models are usually trained and developed in isolated, offline environments. The transition to deployment in a live environment requires input from a range of different specialists, including data engineers, data scientists, and developers. The machine learning model lifecycle encompasses many complex steps including data collection, model training, deployment and ongoing monitoring.
In a business environment, models will be scrutinised by stakeholders without a background in data science, especially if models are being used to make business decisions. In regulated environments like machine learning in finance, decisions made by models can and will be scrutinised by external regulators or customers. This is encapsulated by the concept of machine learning explainability, the ability to explain a model’s decision to a human.
These considerations show how the ongoing operation of a machine learning model is complex, and goes beyond strictly data science. MLOps is used to take a holistic approach to mode management, improving the production of the model as a whole.
MLOps is used to:
- Effectively manage the entire machine learning lifecycle.
- Successfully implement machine learning within an organisation, including scalability, governance, and collaboration between teams.
- Provide a common language to different teams.
- Automate parts of the production cycle, making machine learning more efficient within an organisation.
- Orchestrate the ongoing monitoring and retraining of a model, and its integration within a wider system and data infrastructure.
- Apply best practice DevOps principles such as agile approaches to the machine learning life cycle.
Why do we need MLOps?
MLOps is important as it aims to make the management of all elements of a machine learning model more effective and efficient. Machine learning projects are complex affairs, and the successful deployment in an organisation adds another layer to consider. The approach combines the best practice in the machine learning development cycle, with the relevant areas of an organisation’s overall operation. Elements like data processing and storage are integral to machine learning, but are also vital to the organisation as a whole. MLOps is needed to finetune the machine learning process, in a way that echoes optimisation of all system operations.
MLOps applied the best parts of established fields like DevOps to specifically benefit the machine learning process. Without MLOps, an organisation’s approach to model development, deployment and monitoring would be piecemeal. By taking a combined approach through MLOps, the process can be scaled and made more efficient.
MLOps is needed to:
- Effectively map and improve model workflows across the whole machine learning lifecycle.
- Manage and control the entire process, which will span different teams with different core skills.
- Establish baseline processes and approaches to be applied to different machine learning models within an organisation.
- Strengthen cooperation between teams by establishing a common pipeline.
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