What is MLOps? Benefits, Challenges & Best Practices

The machine learning lifecycle is complex and includes multiple components such as data import, data preparation, model training, model tuning, model deployment, model monitoring, explainability, and more. An ML project calls for collaboration across teams, ranging from data engineering to data science to ML engineering. Strong operational rigor is key for keeping these processes synchronized and operating together.

This is where MLOps comes in. MLOps is a cluster of practices, tools, and processes that allow for experimentation, iteration, and continuous improvement phases of the machine learning lifecycle.

Why is MLOps such a game changer for teams working on ML projects, and how do you actually implement it? Keep reading to find out.

What is MLOps?

MLOps (machine learning operations) is the process of developing new machine learning and deep learning models and running them through a repeatable, automated workflow before deploying them into production.

Data science teams can get a lot of different services from an MLOps pipeline. These include version control for models, continuous integration and delivery (CI/CD), model service catalogs for production models, infrastructure management, live model performance monitoring, security, and governance.

MLOps vs DevOps

MLOps was inspired by DevOps, and the two approaches share some characteristics. However, there are a few areas where MLOps varies greatly from DevOps:

• MLOps is an experiment-driven approach – the majority of data science teams’ activities revolve around experimentation. Teams are continually changing the features of their models to improve performance while also managing an increasing codebase.
• Continuous testing (CT) – In addition to the standard testing phases of a DevOps pipeline, such as unit, functional, and integration tests, an MLOps pipeline must constantly test the model itself, training it and verifying its performance against a known dataset.
• Automatic retraining – In most circumstances, a pre-trained model cannot be deployed directly in production. The model should be retrained and deployed on a regular basis. This requires automating the process that data practitioners can use to train and evaluate their models.
• Performance deterioration – Unlike traditional software systems, even if a model is running flawlessly, its performance might deteriorate with time as a result of unanticipated data properties absorbed by the model, variations between training and inference pipelines, and unknown biases that can increase with each feedback loop.
• Model monitoring – Data monitoring entails more than simply monitoring a model as a software system. MLOps teams must additionally monitor the data and predictions to determine when the model should be updated or rolled back.

The Importance of MLOps

MLOps is critical for managing the ML lifecycle and ensuring that ML models are properly generated, deployed, and maintained.

Without MLOps in place, teams are likely to face several issues:

Issue	Description
Increased error risk	Manual operations can introduce mistakes and inconsistencies into the ML life cycle, compromising the accuracy and dependability of ML models.
Lack of scalability	As ML models and datasets expand in size and complexity, manual procedures become more difficult to handle, making it difficult to efficiently scale ML activities.
Reduced efficiency	Manual methods can be time-consuming and inefficient, hindering the creation and deployment of machine learning models.
Lack of collaboration	Manual procedures can make it difficult for data scientists, engineers, and operations teams to work together successfully, resulting in silos and communication failures.

MLOps tackles these difficulties by offering a framework and collection of MLOps tools for automating and managing the ML life cycle. It lets teams create, deploy, and manage machine learning models more effectively, reliably, and at scale.

Key Components of MLOps

MLOps is made up of multiple components that work together to manage the ML lifecycle:

Exploratory Data Analysis (EDA)

EDA is the process of analyzing and understanding the data that will be used to train the ML model. This includes tasks such as:

• Data visualization is the process of visualizing data to detect patterns, trends, and outliers.
• Data cleaning involves removing duplicate or incorrect data and dealing with missing values.
• Feature engineering, which means transforming raw data into characteristics that are meaningful and useful for the machine learning model.

Data preparation and Feature Engineering

Data preparation and feature engineering are essential components of the MLOps process.
Data preparation involves cleaning, converting, and preparing raw data for model training.

Feature engineering is the process of extracting additional features from raw data to make them more relevant and usable for model training. These procedures are critical for ensuring the ML model is trained with high-quality data and can generate correct predictions.

Model Training and Tuning

Model training and tuning entails training the ML model using prepared data and tweaking its hyperparameters to achieve peak performance.

Common model training and tuning tasks are:

• Choosing the right algorithm – Choosing the correct ML algorithm for the given problem and dataset.
• Training the model – Training the ML model on the training data.
• Tuning the model – Adjusting the model’s hyperparameters to increase its performance.
• Evaluating the model – Evaluating the performance of the ML model on the test data.
• Model review and governance – These guarantee that ML models are created and used properly and ethically.
  • Model validation is the process of ensuring that an ML model fulfills the necessary performance and quality requirements.
  • Model fairness ensures that the ML model does not demonstrate bias or prejudice.
  • Model interpretability is all about ensuring the ML model is intelligible and explainable.
  • Model security ensures the ML model is safe and protected against threats.

Model Inference and Serving

Model inference and serving are all about putting the trained ML model into production and making it available for usage by apps and end users.

• Model deployment – This entails transferring the ML model to a production environment.
• Model serving – Making the ML model accessible for inference by apps and end users.
• Model monitoring – Monitoring the performance and behavior of the machine learning model in production.

Model Monitoring

Model monitoring is the ongoing monitoring of the ML model’s performance and behavior in production.

Relevant tasks can include:

• Monitoring model performance – Tracking parameters such as accuracy, precision, and recall to analyze the effectiveness of the machine learning model.
• Detecting Model drift – Detecting when the machine learning model’s performance decreases over time owing to changes in the data or environment.
• Identifying model issues – Identifying flaws such as bias, overfitting, or underfitting that may impair the effectiveness of the machine learning model.

Automated Model Retraining

Automated model retraining entails retraining the ML model when its performance deteriorates, or fresh data is available.

It involves the following:

• Triggering model retraining – Triggering the retraining process when particular criteria are fulfilled, such as a decrease in model performance or the availability of fresh data.
• Retraining the model – Retraining the ML model using the most recent data and updating the model in production.
• Evaluating the retrained model – This is where you evaluate the performance of the retrained model and ensure it satisfies the necessary performance requirements.

Benefits of MLOps

MLOps comes with several advantages for data teams:

Faster Time To Market

MLOps offers a framework for achieving your data science objectives more efficiently. ML developers may supply infrastructure using declarative configuration files to get projects off to a better start.

Automating model generation and deployment leads to speedier time-to-market and cheaper operating expenses. Data scientists can quickly investigate an organization’s data to provide additional business value to everyone.

Increased Productivity

MLOps strategies improve productivity and speed up the construction of ML models. For example, you can standardize the development or testing environment. Then ML developers may start new projects, switch between them, and reuse ML models across many applications.

Teams can develop reproducible techniques for quick experimentation and model training. Software engineering teams may collaborate across the ML software development lifecycle to increase productivity.

Effective Model Deployment

MLOps enhances troubleshooting and model management in production. Software engineers, for example, can monitor model performance and repeat behavior during debugging. They can track and manage model versions centrally, allowing them to select the best option for various business use cases.

Integrating model processes with continuous integration and continuous delivery (CI/CD) pipelines limits performance deterioration while maintaining model quality. This applies even after updates and model adjustments.

How to Implement MLOps

MLOps deployment is classified into three stages based on your organization’s automation maturity.

MLOps Level 0

Level 0 is characterized by manual ML processes and a data scientist-driven procedure for firms that are new to machine learning technologies.

Every step is done manually, including data preparation, machine learning training, and model performance and validation. Each stage is executed and handled interactively, and the transition between them must be done manually. The data scientists often deliver trained models as artifacts, which the engineering team puts on API infrastructure.

The method separates the data scientists who design the model from the engineers who install it. Due to the few releases, data science teams may only retrain models a few times each year. When combined with other application code, ML models do not require CI/CD considerations. Similarly, no active performance monitoring occurs.

MLOps Level 1

Teams looking to train the same models with new data usually require level 1 maturity implementation. MLOps level 1 attempts to constantly train the model by automating the ML workflow.

In level 0, you put a trained model into production. In contrast, for level 1, you set up a recurring training pipeline to feed the taught model to your other apps. At the absolute least, you ensure the model prediction service is delivered continuously.

Level 1 maturity includes:

• rapid ML experiment phases with substantial automation,
• continuous training of the model in production, using fresh data as live pipeline triggers,
• consistent pipeline implementation across development, preproduction, and production environments.

Engineering teams collaborate with data scientists to develop modularized code components that are reusable, composable, and possibly shared across several machine learning pipelines. They can also set up a centralized feature store to standardize feature storage, access, and definition for machine learning training and serving.

MLOps Level 2

MLOps level 2 is designed for teams looking to experiment more and generate new models that require ongoing training. It’s ideal for companies that update their models in minutes, retrain them hourly or daily, and redeploy them across thousands of servers.

Due to the presence of multiple ML pipelines, an MLOps level 2 configuration requires the completion of all MLOps level 1 setups. It also requires an ML pipeline orchestrator and a model registry that tracks various models.

Several ML pipelines repeat the following three steps at scale to ensure a continuous supply of the model:

Steps	Description
Build the pipeline	You iteratively test novel modeling and machine learning algorithms while ensuring that experiment stages are organized. This stage generates source code for your ML pipelines. You save the code in a source repository.
Deploy the pipeline	Next, you compile the source code and perform tests to get pipeline components ready for deployment. The result is a deployed pipeline that includes the updated model implementation.
Serve the pipeline	Finally, you provide the pipeline as a prediction service to your applications. You get statistics on the installed model prediction service using live data. This stage output serves as a trigger for running the pipeline or starting a new experiment cycle.

Key Challenges in MLOps

Data Management

As much as data quality affects machine learning models, it also presents a significant difficulty when developing and applying MLOps.

Data inconsistencies are one of the most common issues. Data formats and values often differ because data must be acquired from several sources. For example, although current data may be easily retrieved from an existing product, past data can be obtained from the client. Such mapping disparities, if not addressed effectively, might have a severe impact on the overall performance of the machine learning model.

Another one is the lack of data versioning. Because data continually changes, the results of the same machine learning model may differ significantly. Data versioning takes numerous forms, including distinct processing techniques and new, updated, or deleted data. The model will not run well unless it is versioned efficiently.

Privacy and Security

Machine learning solutions often handle sensitive data. As a result, environmental protection is critical to the long-term survival of the machine learning organization.

The most common security concern is using outdated libraries. Teams are often unaware that this can lead to a multitude of security flaws that allow hostile attackers to get access. Another security problem is that model endpoints and data pipelines are not appropriately secured. These are occasionally made public, which may expose sensitive info to third parties.

Security may be a difficult issue in any MLOps environment; this is why having software that offers security patching and support is critical for your project’s survival and deployment to production. It’s also advised to use multi-tenancy technology to secure both the internal environment and data privacy.

Inefficient Tools and Infrastructure

Since ML models are mostly research-based, extensive testing is essential to determine the best approach. However, performing tests may be disruptive and costly on corporate resources.

Different data versions and processes must run on hardware that can perform complex computations quickly. Furthermore, novice teams typically test on notebooks, which is inefficient and arduous.

Development teams might request budgets for virtual hardware subscriptions like those on AWS or IBM Bluemix if hardware is an issue. Regarding notebooks, developers should make it a habit to test scripts since they are easier and more efficient.

Communication and Culture Issues

MLOps needs a culture of collaboration and cooperation among several teams, including data scientists, data engineers, and operations team members. This can be difficult, especially in firms not used to functioning this way.

A major problem in this space is failing to let end users understand how an ML model works or which algorithm is providing insight. After all, this is a complicated topic that requires time and knowledge to comprehend. If people don’t comprehend a model, they are less likely to trust it and adopt its insights

Organizations may avoid this issue by including clients early in the process and asking them what problem the model should answer. They should also show and explain model findings to users regularly and let them provide input during the model iteration.

High Costs

MLOps may need a large financial and time investment. To make MLOps effective, firms must be willing to spend money on the necessary technology and resources. A machine learning platform can take anything from a few months to two years to build, depending on the number of engineers involved.

To overcome financial constraints, data science teams must consider the business side and do a rigorous cost-benefit analysis of restrictive provisions vs the return on investment from practical solutions that can operate under such provisions.

Best Practices for MLOps

Version Control for Data and Models

The machine learning development process is iterative, with teams searching for the best-performing model while adjusting hyperparameters, code, and data. Retaining a record of these modifications is critical to tracking model performance relative to the parameters, saving you the time spent retraining the model for experimentation.

A version control system provides a changelog, which might be useful when your model fails and you need to roll back your modifications to a stable version. By capturing snapshots of the complete machine learning process, you can duplicate the identical output, including the learned weights, saving time on retraining and testing.

Dependency monitoring entails tracking several versions of datasets (training, assessment, and development), as well as tweaking model hyperparameters and parameter values. Version control also enables you to test several models on separate branches or repositories, modify model parameters and hyperparameters, and track the correctness of each change.

Automation of the Model Lifecycle

Automation is intimately linked to the notion of maturity models. Advanced automation allows your organization’s MLOps maturity to increase. However, many tasks within machine learning systems still require manual effort: data cleaning and transformation, feature engineering, separating training and testing data, creating model training code, and more.

Because of this manual approach, data scientists may face a larger risk of mistakes and squander time that may be better spent experimenting.

Continuous training, in which data teams create pipelines for data analysis, ingestion, feature engineering, model testing, and so on, is a popular type of automation. It prevents model drift and is often recognized as the first stage of machine learning automation.

Data scientists can save time and money by automating data validation, model training, testing, and assessment. Future projects or phases may employ a productized automated ML pipeline to provide accurate predictions on new data.

Continuous Integration and Continuous Delivery (CI/CD) for Models

MLOps relies heavily on CI/CD principles borrowed from DevOps.

Continuous Integration guarantees that code changes and upgrades are merged and tested frequently, allowing issues to be identified early in the development cycle. Continuous Deployment automates the deployment of verified models to production settings, saving manual work and mistakes.

You can build in these processes using tools like Jenkins, CircleCI, and GitHub Actions, allowing quicker iteration and deployment cycles.

Monitoring and Observability of Models in Production

When an ML model employs error-prone input data, its accuracy decreases. Monitoring ML pipelines ensures that the data sets entering the ML model remain clean throughout business activities.

This is why it’s a good idea to automate continuous monitoring (CM) tools to detect declines in real-time model performance and make necessary changes on time. In addition to monitoring data set quality, these tools may also track model assessment parameters such as response time, latency, and downtime.

Collaboration Across Cross-Functional Teams

Collaboration between data scientists and machine learning engineers is critical to a successful ML project.

Data scientists should constantly improve their code-writing abilities to contribute directly to production-ready solutions. This helps to reduce barriers and provide a smoother transition from the research phase/prototypes to real and production-ready pipelines.

On the other hand, machine learning engineers must address the non-technical aspects. The product is designed to meet the business’s needs. Following that, product questions arise, and machine learning operations begin. Understanding consumer requirements, industry trends, and corporate objectives helps develop superior solutions that are properly customized to these goals.

MLOps Use Cases

Finance: Fraud Detection and Risk Management

MLOps offers various applications in the finance industry, such as fraud detection, risk management, and tailored financial services. For example, It can identify fraud in real time by examining transaction data and detecting fraudulent trends. It may also be used to create credit risk prediction models to help financial organizations make better loan decisions.

Healthcare: Predictive Analytics and Diagnostics

One of the most significant issues for healthcare providers is ensuring the accuracy and dependability of their models. This is crucial for constructing illness prediction models, as erroneous positives or negatives can have catastrophic effects.

MLOps tackles this issue by outlining a methodology for model testing and validation. This guarantees that the models are reliable and accurate. MLOps can help anticipate disease outbreaks, improve patient monitoring, and lower expenses.

For example, MLOps may be used to create healthcare analytics models. These models enable healthcare organizations to examine electronic health records (EHR). It also allows them to forecast which patients will likely acquire specific illnesses. This enables healthcare practitioners to take preventative actions and tailor treatment to high-risk patients.

Manufacturing: Predictive Maintenance

One of the most difficult tasks in manufacturing is forecasting machine breakdowns. However, MLOps provides a solution by allowing for predicting and avoiding equipment faults. This is accomplished through predictive maintenance, which trains machine learning models on past data to identify trends that indicate upcoming breakdowns.

How lakeFS Works with MLOps

In the world of ML, controlling the output is just as important as the input: the data itself. We already mentioned version control as one of the key aspects of ML projects. 

The open-source solution, lakeFS, brings version control to the data world. It enables teams to manage their data using Git-like procedures (commit, merge, etc.) while growing to billions of files and petabytes of data.

One of the most important lakeFS capabilities is environment isolation.

Using lakeFS, many data practitioners may work on the same data, creating a different branch for each experiment. Data may be tagged to represent individual experiments, allowing them to be reproduced with the same tag.

Once the update works for you, you may push it or merge it back into the main branch and make it available to customers. Alternatively, you may instantly revert changes without going through each file individually as with S3. You can reverse the modification and return to the last excellent condition.

This is how lakeFS’s data version management allows several data practitioners to operate on the same data.

Conclusion

The future of MLOps is extremely promising. As machine learning (ML) grows, teams will build strong and effective operational processes by finding and evaluating new trends, putting them into action, and proactively dealing with the problems that come up because of them. There is a reason why we’re seeing trends like LLMOps appearing in the space to support teams working on particular branches of ML.