MLOps: a guide to machine learning model management

Gartner expects that by 2025, 70% of organizations will have operationalized AI architectures due to the rapid maturity of AI orchestration initiatives.
-- Laurence Goasduff, Gartner Contributor

When you hear statistics like this, it can feel alarming–how does one even begin to tackle operationalizing AI and machine learning? Even worse, when you hear “machine learning,” you might picture really advanced AI models, neural nets, or just something of a black box–esoteric and hard to understand. Some of this is true. For a data scientist or machine learning engineer, you need to understand some complex math, and to have a sense of how algorithms like random forests and decision trees work under the hood. But in actuality, most of the work in the application of successful machine learning relies not just on the algorithms or the models themselves, but the logistics of it all. Many of these logistics fall under the umbrella of machine learning model management, or MLOps. In this article, we'll cover why MLOps is important, the AI project life cycle, challenges with MLOps, and techniques and tools to implement. Once you have a clear and organized process, MLOps can save you and your team time by preventing errors or even failures before they happen.

MLOps are key to successful machine learning projects

MLOps, or machine learning operations, is a set of practices and tools that help organizations manage the logistics of machine learning projects. MLOps helps to ensure the reliability and performance of machine learning models by providing a process for monitoring, testing, and evaluating models in production. This can help to identify and fix problems and ensure that the models are meeting business needs.

Implementing MLOps speeds up the development and deployment of machine learning models. By automating tasks such as testing and model evaluation, organizations can reduce the time and effort required to deploy and maintain models. A key component of MLOps is continuous integration and deployment so that teams can make necessary changes to models more quickly and easily.

Besides improving the reliability and speed of machine learning projects, MLOps also helps to promote collaboration and communication within the team. By providing a simple process for managing machine learning models, MLOps helps to reduce misunderstandings and miscommunications and encourage teamwork. This can lead to more effective collaboration and ultimately better results.

MLOps also helps to improve the maintainability and scalability of machine learning models. By implementing processes such as version control and automated testing, organizations can more easily make and track changes to models. This can help to ensure that models are consistently performing well and can scale with the needs of the business.

AI project lifecycle steps

The AI project life cycle involves several steps, each with its own challenges and difficulties. Understanding and effectively managing each step is crucial for the success of a machine learning project.

Identify a business objective

As with any problem, you need to understand your objective. This involves clearly defining the business problem or challenge that you hope to address with your machine learning project. It's important to clearly understand the problem you are trying to solve, as this will help guide the rest of the process and ensure that your machine learning project is focused and relevant.

Next, determine the SMART goals and objectives for the project. These goals should be specific, measurable, achievable, relevant, and time-bound (SMART). Having clear goals and objectives will help ensure that you are working towards a specific, achievable result with your machine learning project.

Only when you have articulated your ultimate objective, as well as the smaller SMART goals you need to achieve can you develop a plan. This plan should include specific steps and tasks that need to be completed in order to achieve the desired results. It's important to have a clear, actionable plan in place to guide the rest of the process and help ensure the success of your machine learning project.

Collect and prepare data

Now that you have clear goals in mind, you need to identify the data required to complete your project. This involves determining what types of information apply to your machine learning project and how that data will be used. You may need to revisit this step at a later stage in your project as it grows. Remember to always go back to your business case as the north star guiding your work.

Once you have identified the data needed for the project, the next step is to determine the sources of that data. This involves identifying where the data is coming from and how it will be accessed. It's important to consider the reliability and quality of the data sources and to ensure that you have the permissions and access to collect the data.

Then you can actually collect and prepare the data. This involves actually retrieving the data from the sources and then cleaning and transforming the data for use. Data cleaning can involve tasks such as removing duplicates, filling in missing values, and formatting the data correctly. It's important to have a thorough and robust data cleaning and data preparation process in place to ensure that you are working with high-quality data.

Einblick for data cleaning and EDA

At Einblick, we work to make this part of the process as easy and painless as possible. Your time as a data scientist is precious, as are the processes of cleaning and preparing data. We’re reimagining data science with a canvas-based approach so that you can easily:

Code in Python and SQL, switching as needed
Create visualizations that allow you to pivot throughout the exploratory data analysis and data cleaning processes
Branch into different thought processes and compare output side-by-side via canvas layout

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Einblick’s AutoML helps automate model building

Based on 6 years of research at MIT and Brown University, Einblick’s AutoML cell helps you to quickly generate, compare, and evaluate multiple machine learning pipelines. After they have been created, you can save the Python code used to create the pipelines, and continue tuning by hand as needed. AutoML will get you reliable results in seconds due to Einblick’s progressive computation engine.

Included in Einblick’s AutoML is an executor and an explainer.

The executor will let you apply your chosen ML pipeline to new data.
The explainer will allow you to gain a deeper understanding into how the model works, what features are driving the model predictions, and more.

Even better, Einblick focuses on collaboration. No one individual can hold all the expertise–technical and domain–necessary for using complex ML models at scale. As a result, communication and collaboration are essential.

Live collaborate with teammates and stakeholders.
Share canvases and dashboards by toggling permissions.
Stop getting siloed, and move forward as a team.

Build and select a model

Building a model, like any other part of the AI life cycle involves a few components. The first step in building a model is to determine how you are evaluating your model. Later on you will actually evaluate the model for accuracy and efficacy, but understanding which metrics are important early on will help guide the rest of the process. Next, you have to choose the machine learning algorithm. This involves selecting an algorithm that is well-suited to the problem you are trying to solve and the type of data you are working with. There are many machine learning algorithms to choose from, and it's important to deliberate the pros and cons of each one to ensure that you select the right one for your project. Model selection is a big part of machine learning, and it’s a pretty complex topic. There are tons of ways to evaluate different machine learning algorithms and pipelines, so it’s important to be sure of your decision before putting the pipeline into production.

Once you have chosen the machine learning algorithm or the several to evaluate, the next step is to feed the data into the model. Depending on if you’re using supervised or unsupervised learning, this process can look different. How much training data are you giving it? How many features are you selecting? Which features have the most impact on your model? How are you tuning the parameters of the model?

After you’ve built your models, you can evaluate the performance of the model or models. This can involve testing the model on a set of data that it has not seen before and measuring its accuracy and other performance metrics. It's important to carefully evaluate the model's performance to ensure that it works as expected and is ready for deployment.

Deploy a model

The fourth step in the AI project lifecycle is to deploy the model. This involves choosing the deployment platform, testing the model in a live environment, and monitoring the performance of the model in production. It's important to deliberate the deployment platform and to monitor the model's performance to ensure it works as expected.

Iterate on the model

The last step in the AI project lifecycle is to iterate on the model. This involves monitoring the performance of the model over time, identifying areas for improvement, and iterating on the model to improve its performance. It's important to regularly evaluate the model's performance and make adjustments as needed to keep it performing at its best.

Components of MLOps

Now that we understand the steps involved in the AI project lifecycle, let's delve into the various components of machine learning model management.

Data management

One key component of MLOps is data management. This includes everything from collecting and cleaning the data to storing it and making it accessible to the team. It's important to have a well-organized, reliable system for managing your data, as this will impact the quality and usefulness of the data for your machine learning models.

Model development and testing

Another important component of MLOps is model development and testing. This includes choosing the right machine learning algorithm, training and testing the model, and evaluating its performance. It's important to have a robust process in place for developing and testing your models to ensure they are accurate and reliable.

Model deployment and monitoring

A third component of MLOps is model deployment and monitoring. This involves choosing the deployment platform, testing the model in a live environment, and monitoring its performance in production. It's important to have a system in place for deploying and monitoring your models to ensure they are running smoothly and meeting the needs of your business.

Iterative improvement

Finally, and perhaps most importantly, is iterative improvement. Data and business needs can change, and your models and how they work in production have to adapt as needed. This involves regularly evaluating the performance of your models, identifying areas for improvement, and iterating on the models to improve their performance. It's important to have a process in place for continuous improvement to ensure that your models are always performing at their best.

The challenges of machine learning model management

Poor data quality

One of the key challenges of machine learning model management is the lack of data quality or accessibility. Poor quality data, or data that is difficult to access, can significantly impact the performance and reliability of machine learning models. For example, if the data is incomplete or contains errors, it can lead to inaccurate or biased models. Similarly, if the data is difficult to access, it can be time-consuming and resource-intensive to gather and process new data, slowing down the development of the model.

Complexity

Another challenge is the complexity of machine learning algorithms. Some algorithms can be difficult to understand and interpret, making it hard to properly train or determine the cause behind any problems. This can be especially challenging for organizations that do not have in-house machine learning expertise or resources.

Lack of expertise

Lack of expertise or resources can also be a challenge in machine learning model management. Building and deploying machine learning models often requires specialized knowledge and resources, such as data scientists, machine learning engineers, and computing infrastructure. If an organization does not have these resources in-house, it can be difficult to effectively manage machine learning models. But technical expertise will only get you so far in machine learning, you also need domain expertise to understand why the data is the way that it is, where it came from, and how it’s being used. In main cases, domain experts will not also be the technical experts, and in those cases, communication and collaboration are even more important.

Opaque models

Finally, limited visibility into model performance can be a challenge in machine learning model management. It can be difficult to get a clear understanding of how machine learning models are performing in production, making it hard to identify and fix problems. This can lead to models that are not meeting business needs or that are not providing value.

Techniques and tools for effective MLOps

Automated testing and evaluation

Automated testing and evaluation of machine learning models can be a crucial step in the model management process. It helps to ensure that the models are performing as expected and can identify any issues or problems that may have occurred during training or deployment.

I can automate parts of the testing and evaluation process, such as splitting the data into training and testing sets, using cross-validation, or comparing metrics such as accuracy, precision, and recall, which evaluate the model's performance. By automating this process, organizations can save time and resources, and quickly identify and fix any issues that may arise.

Continuous integration and deployment

Continuous integration and deployment is a practice that involves regularly integrating code changes and deploying them to production environments. This approach can be useful for organizations as updates to existing models and new models can be pushed out as soon as possible.

We can implement continuous integration and deployment via platforms like CircleCI, which automate the process of deploying code changes. By using this approach, organizations can speed up the development and deployment of machine learning models and ensure that they are able to quickly respond to changing business needs.

Model tracking and monitoring

Model tracking and monitoring is essential for ensuring that machine learning models are performing as expected and meeting business needs. By tracking and monitoring models in production, organizations can identify any issues or problems that may arise and take corrective action. This can be done using log analysis, monitoring metrics, and alerting systems. By regularly monitoring and tracking models, organizations can ensure that their models are delivering the desired results and identify any opportunities for improvement.

Data pipelines

Data pipelines are an essential part of the machine learning model management process, as they provide a way to efficiently and reliably transport data from source systems to the machine learning models. Data pipelines can be built using a variety of tools and techniques, such as batch processing or streaming data platforms.

By building efficient and reliable data pipelines, organizations can ensure that the data used to train and test machine learning models is of high quality and free of errors or inconsistencies. This can help to improve the accuracy and performance of the models, and reduce the time and resources required to manage them.

Data and model versioning

Data versioning is the practice of keeping track of different versions of data used to train and test machine learning models. Similarly, model versioning is the practice of keeping track of different versions of machine learning models. This can be useful for debugging issues or problems with the models, as it allows organizations to compare different versions of the data or model to see how it may have changed. Version control for data or models can be implemented using tools such as DVC, which allow organizations to track changes to data sets and models and roll back to previous versions if necessary.

Model interpretability and explainability

Interpretability and explainability are important because machine learning models can seem like a black box, which can cast doubt on its predictions. By providing some interpretations of the model results and reasoning behind the predictions, you add trustworthiness and transparency to your process. You can help organizations understand why a model is making certain predictions, and also identify any biases or errors in the model's decisions. There are a variety of techniques and tools available for improving the explainability of machine learning models, such as feature importance, partial dependence plots, and local interpretable model-agnostic explanations (LIME).

Model performance optimization

Model performance optimization involves identifying ways to improve the accuracy and efficiency of machine learning models. Hyperparameter tuning and feature engineering are two umbrella tasks that can help you optimize your model performance so that you are delivering the best possible results and meeting business needs.

Model governance

Model governance is the practice of establishing and enforcing policies, procedures, and standards for the development, deployment, and management of machine learning models. This can include activities such as defining roles and responsibilities for model development and management, establishing guidelines for model development and deployment, and implementing processes for monitoring and evaluating model performance. All of these efforts are important for ensuring that machine learning models are reliable, accurate, and aligned with business objectives.

Model serving

The practice of deploying machine learning models in production environments via an API, giving others access to the use of the model and its valuable predictions, is called model serving. There are a variety of tools and frameworks available for serving machine learning models, including TensorFlow Serving and Seldon. By implementing model serving, organizations can make machine learning models available to users or systems in real-time, and easily update or roll back models as needed.

Model deployment automation

Model deployment automation is the practice of automating deploying machine learning models to production environments. This can be useful for reducing the time and effort required to deploy models, and for ensuring that models are consistently deployed in a controlled and reliable manner. Model deployment automation can be implemented using tools such as CircleCI, which automate building, testing, and deploying code changes.

Model bias detection and mitigation

Model bias refers to the tendency of a machine learning model to make inaccurate or unfair predictions or decisions because of underlying biases in the data used to train the model. Model bias detection and mitigation involves identifying and addressing biases in machine learning models to ensure that they are fair. You can use fairness metrics, bias mitigation algorithms, and data augmentation to assist in solving these issues.

Key takeaways

MLOps are the logistics that underpin any productionalized machine learning.
MLOps is critically important if you want to harness the power of machine learning and AI for your business.
Creating a system for managing the logistics of machine learning in production starts with the basics–know your business problem, know your data, and know your models. Build a system so you can re-evaluate and iterate as needed.
ML models can be a black box, so having tools that include explainers and collaboration features is critical for stakeholder buy-in and team engagement.