MLOps: What Is It, and How Can It Help Your Business?

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If you want your business to become data-driven, then most likely you will start with AI and ML development services. Thankfully, there is a way to build your own ML model without unnecessary hurdles and overwhelming costs.

Today, we want to discuss machine learning operations and how they can be beneficial for you.

What Is MLOps?

Machine learning operations (MLOps) are a set of methods to make ML model development as efficient as possible. They combine best practices from development operations (DevOps) with machine learning engineering. The result? Streamlined development, robust model monitoring, and, more importantly, cost efficiency.

MLOps can be split into phases.

• Problem Definition: Before development starts, you need to define the problem you want to tackle with your machine learning model and how it will approach this problem.
• Data Collection and Preparation: The more data you gather, the better it is for model training. Do remember that you need to label and structure it properly.
• Model Development: At this point, you start creating and training your machine learning model. Let your development team’s imagination fly — encourage experimentation to find the best-performing algorithm that will fit you.
• Model Evaluation: Remember when we defined the problem? Now, it’s time to evaluate your model’s performance using the metrics you defined at that phase.
• Model Deployment: Set up your infrastructure and get ready for the model to hit production! However, your work is not done yet.
• Monitoring and Maintenance: Now that your model is live, you need to maintain it. A good practice is to monitor it as well. This way, you can gather valuable insights that will be useful in the future.
• Feedback: This phase is pretty much self-explanatory. Gather feedback about the model performance. Use the collected data for developing updates, model training, and addressing issues that might have occurred. Conducting exploratory data analysis is crucial.
• Scaling: It’s time for your model to start handling larger datasets. This process will involve a lot of pipeline optimization for future cost-efficiency, among other things. Machine learning workloads are defining points in evaluating performance.
• Governance: Ensure your machine learning model complies with regulatory standards. Pay attention to its privacy and security.
• Collaboration: Encourage collaboration between data scientists, developers, and maintenance teams. Knowledge sharing will improve the model’s general health and spread the use of best practices.

That’s all the phases. The important thing about them is that they are iterative, meaning your MLOps team will cycle through them several times. This ensures continuous integration, allowing you to improve it as much as you want.

6 Key Benefits Of Using MLOps

The main MLOps advantages are increased productivity, cheaper model development, reproducibility, reliability, risk reduction, and ease of monitoring. How exciting is that? Read more about each of these benefits below.

Benefit 1: Increased productivity

There are two ways MLOps improves productivity:

• Automation. When developing a machine learning model, you will encounter a lot of repetitive tasks. MLOps helps automate your workflow and prevents potential human input errors.
• Standardization. While using MLOps, you’ll quickly notice another pleasant surprise in the form of standardization. It allows for better compatibility between different models and will come in handy during version control.

Benefit 2: Cheaper model development

MLOps significantly cut the costs of machine learning projects development through all work stages:

• Automation lowers manual efforts, which enables your employees to do more complex and creative tasks.
• Decreased number of errors also reduces costs since there’s less stuff to fix.

Benefit 3: Reproducibility

As we already mentioned, MLOps is iterative. This leads to the following.

• Data versioning. Every dataset created has multiple snapshots (versions) from a specific point in time with specific data inside. With data versioning, you can get back to the past at any given moment.
• Model versioning. By the same principle, every model created has multiple versions. Each of the model versions captures the model in different development stages, capturing features built at these stages.

Benefit 4: Reliability

Reliability is guaranteed by:

• Streamlined processes. One of the biggest challenges during ML development is scaling from a small model to a larger system. By incorporating DevOps principles, MLOps streamlines model management, which results in reliable scaling.
• Automation. ML pipeline automation reduces the chances of human error, making models more reliable. Thanks to automation, MLOps allows for quick scaling of model management procedures.

Benefit 5: Easy monitoring

Monitoring machine learning models’ behavior and performance is essential because they can change because of shifts in the real-world environment. But that’s not all monitoring is useful for.

• Model retraining. By constantly retraining ML models, you can provide consistency in its outputs. To make this process easier, MLOps provides automation for retraining and monitoring the model after specific events in the system.
• Alerts. MLOps can also share the real-time status of your model and data by alerting data scientists’ teams in case of model drift. This allows for a quick response against model degradation.

Benefit 6: Risk reduction

When it comes to risks, there are two general risks you might encounter:

• Machine learning models often need regulation and drift checks. It is easy to oversee some of the industry regulations or when the model starts to drift. Fixing it could be very time-consuming, but not with MLOps. It enables greater transparency and ensures compliance with industry regulations.
• MLOps focuses on reducing the risk of deploying faulty or biased models in production environments. You can achieve it by implementing robust testing and quality assurance processes.

In our original article, we also cover how different participants, from developers to stakeholders, benefit from MLOps implementation. Discover more details via the link.