What Is MLOps and Why Do We Need It?

If you are someone who is passionate about software engineering and machine learning, you must have noticed the gap between building machine learning models and successfully deploying them into any website or production environment. This is where MLOps comes in. In this blog, we will walk you through what MLOps is, why it is critical in the real world, and how it is playing a major role in shaping the features of machine learning applications. So, let’s get started!

Table of Contents

• What is MLOps?
• MLOPs Life Cycle
• Why do We Need MLOps?
• Basic Principle of MLOPs
• Difference between MLOps Vs. DevOps
• What problems are solved by MLOps?
• Challenges in Implementing MLOps
• Best Practices for MLOps
• Conclusion

What is MLOps?

By the MLOps’s definition it refers to the process of integrating ML workflow with DevOps concepts. MLOps is the short form of Machine Learning Operations. It refers to a set of practices that is responsible for managing the entire life cycle of a machine learning model. This cycle involves building the model and deploying it, and then keeping an eye on how the model is performing and updating the model when needed. All the steps in the cycle are well-organized, and it is a step-by-step process.

MLOPs Life Cycle

The MLOps life cycle consists of 9 stages. All these 9 stages are explained below:

1. Problem Definition

While you are developing the models, the first step is to identify the model that needs to be solved with AI.

2. Data Collection

After you have identified the problem, next comes the data collection phase. Here, the data you have collected will be used to train the models, and they should come from an appropriate source.

3. Data processing and storage

For training your models effectively, a large amount of data is required. For storing data, data warehouses or data lakes are normally used. Once they are stored, you need to organize and clean the data. This can be done either all at once (in the form of batches), or continuously as it comes in (streaming). It all depends on what works best for you.

4. Metrics Definition

For measuring the quality of the models and for determining if the models are successful in solving the problem that was identified in the first step, it is important for your team to agree with you regarding what metrics are to be used.

5. Data Exploration

In this step, data scientists are given the responsibility to come up with ideas regarding which machine learning methods work best. These ideas are given based on patterns or insights they find during analyzing the data.

6. Feature extraction and engineering

The step in the MLOps life cycle is to figure out the specific parts of the data that will be used for training the models. For example, if you are building a model that will decide whether an individual should be approved for a loan, one of the features of the model might include the credit score of the users. You should also figure out how to create or calculate the credit scores of the users. Since new data keeps coming, it is important to make sure that these features are updated regularly so the model remains accurate for a long time.

7. Model Training and Offline Evaluation

After the models are built, trained, and tested, you have to choose the model that performs the best. To do this properly, you have to divide the data into 2 parts. About 80% to 90% data is used to train the models, which helps the model to learn patterns. The remaining 10% to 20% data is used to test the model on how it performs on new, unseen data. This will help you to make sure that the model does not memorize the training data, but you can make good predictions with the model in real-world scenarios.

8. Model integration and deployment

After you have trained and evaluated the models, you have to integrate them into a product and then deploy them in the next step. You can do this on a cloud system like AWS or Azure. This process involves building new services that help the product interact with the model and get predictions. For example, when you use the loan approval model, the website on which this model is integrated will find a way to access the assessment of the algorithm regarding who should be approved for the loan and who should not.

9. Model release and monitoring

Once you have deployed the model, you need to monitor it closely to ensure that there are no issues with the model. Another benefit of monitoring models is that you can identify how the performance of the model can be improved by retraining it with new data.

Why do we need MLOps?

MLOps is crucial because it tackles the particular difficulties associated with installing, overseeing, and sustaining the machine learningmodels in practical contexts. We require MLOps for the following reasons:

1. Continuous Integration and Delivery (CI/CD)

MLOps support the implementation of CI/CD methods for ML models, thus allowing developers to create, test, and deploy their models automatically.

2. Model monitoring

Model monitoring is important because ML models are dynamic, which means that they regularly change over time. Monitoring the performance of the models in production and spotting the problems that need fixing are made easier with the aid of MLOps.

3. Collaboration

With the use of MLOps, it is easier for you to keep track of everything that is related to your machine learning models. This process of tracking is used especially in industries like finance and healthcare. In these industries, it is important that you follow strict rules and that you are able to show how and why a model was built in a certain way.

4. Scalability

While you are working with ML, you must have often struggled to reproduce results. This is where MLOps turns out to be really useful. It focuses on reproducibility, which means that the models and experiments can be repeated and give the same results. MLOps is also helpful in scaling things up. This helps a model that was trained on a small dataset move to a much larger system.

Basic Principles of MLOps

Machine learning development pipelines undergo changes at three levels: data, machine learning model, and code. MLOps principles are designed to impact ML-based software at one of these three levels.

The principles of MLOps focus on the following aspects:

1. Versioning: ML scripts, models, and datasets are treated as crucial components of DevOps processes. The MLOps approach involves tracking the versions of data and models using system controls and alerting mechanisms for any changes.

2. Testing: Testing is essential at all levels of machine learning systems, but the scope varies depending on ensuring performance and expected outcomes. MLOps principles emphasize thorough testing to validate the functionality and effectiveness of ML systems.

3. Automation: The level of automation employed determines the maturity of an ML initiative. MLOps teams aim to automate the deployment of ML models, streamlining the process and increasing efficiency.

4. Reproducibility: Achieving reproducible and consistent results in a machine learning workflow, given the same input, is a fundamental MLOps principle. It ensures that ML experiments and processes can be replicated reliably.

5. Deployment: Model deployment should be based on effective experiment tracking, which includes utilizing feature stores, containerizing the ML stack, and having the flexibility to run models on-premises, on the cloud, or at the edge. MLOps principles advocate for a well-managed and adaptable deployment strategy.

6. Monitoring: It is crucial to ensure that ML models perform as expected once deployed. Monitoring is vital in tracking changes related to dependencies, data, source systems, and upgrades. MLOps principles emphasize continuous monitoring to detect any anomalies or issues promptly.

MLOps principles provide guidance and best practices for managing and optimizing machine learning development pipelines, covering versioning, testing, automation, reproducibility, deployment, and monitoring aspects of ML-based software.

Difference between MLOps Vs. DevOps

Here’s a table that outlines the key differences between MLOps and DevOps.

What problems are solved by MLOps?

Like DevOps, MLOps also helps you to improve the quality of your production model while you use the model for any business purposes. A few problems that are solved by MLOps are given below.

1. Inefficient Workflows

MLOps provides you with a clear and efficient way for managing every step in the machine learning process, which includes building models and maintaining the models for deployment. It helps you to connect business knowledge with technical skills. This makes the entire workflow organized and repeatable.

2. Failing to comply with regulations

Machine learning is still a field that is undergoing development, and its rules and regulations keep changing. MLOps helps you to adapt to these changes, like the ones in the banking industry. They have to make sure that their ML systems stay responsive to the changes and follow the latest guidelines.

3. Bottlenecks

When complex algorithms are used by machine learning models, it leads to many problems or delays (bottlenecks). MLOps plays a huge role in fixing this by encouraging better teamwork between the operations team and the data team. These teams share their knowledge by working together, which makes it easier and faster to build, test, monitor, and then deploy the models without any major issues.

4. Better governance and compliance

A framework for tracking, observing, and auditing ML models is provided by MLOps. Organizations may use this to comply with legal obligations and ensure that their models are ethical, clear, and comprehensible.

Challenges in Implementing MLOps

Some of the challenges that might arise while implementing MLOps are given below:

• Lack of standardization: There are many organizations currently that don’t have a consistent process for managing machine learning workflows.
• Integration with Legacy Systems: The already existing infrastructures may not support modern MLOps tools easily.
• Limited Team Expertise: Many teams nowadays lack the specialized skills which are required for the implementation and maintenance of MLOps pipelines.
• Tool Overload: Choosing the right stack can be a challenging task with so many MLOps available today.
• Data Quality Issues: Poor-quality or inconsistent data can affect the performance of the model and its reliability.
• Cultural Resistance: Many teams may resist the changes in workflow or any collaboration introduced by MLOps practices.
• Scalability concerns: To scale models from experimentation to production,  it requires robust infrastructure and planning.
• Security and Compliance: For maintaining data privacy and following the rules and regulations of an organization, it can be difficult without the presence of any dedicated processes.

Best Practices for MLOps

Some best practices for MLOps include:

• You should treat models as products instead of one-time deliverables.
• You have to automate everything, from data pipelines to model deployment.
• You should use version control for both code and data
• You have to monitor models consistently in production.
• You should encourage collaboration across teams by using standardized tools and documentation.

Conclusion

MLOps assist businesses in developing and deploying ML models more successfully, effectively, and ethically. It could help businesses in staying one step ahead of the competition and foster innovation in a sector that is going through a rapid transformation.

If you are interested in learning how MLOPs can transform your application, then do check out our Data Science Course.