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Steps to Implement a Live Project in MLOps

Steps to Implement a Live Project in MLOps

Companies across all sectors are starting to realize the widespread benefits of AI and machine learning operations. Implementing a live project using MLOps might sound too complicated to complete without professional help. However, your team can do this successfully if you take the time to understand the components and steps involved. As with all aspects of machine learning, the steps of completing any given project are specific and exacting, but if you go about the process carefully, you should end up with good results.

The first thing you’ll need to do is familiarize yourself with the basic concepts of MLOps. Gain an understanding of the elements that make MLOps work, who needs to be involved in a project, and what the basic steps are.

What’s MLOps? What are MLOps projects?

MLOps are machine learning operations. Machine learning is a branch of artificial intelligence that is devoted to making machines imitate human behavior. Machine learning operations, then, are practices that employ machine learning models in reality.

MLOps also incorporate elements of DevOps. DevOps, or development operations, are operations that combine software development and IT operations in an effort to streamline software quality and efficiency. It works alongside agile software development, which is the methodology through which DevOps functions. However, traditional DevOps do not suffice for MLOps projects as these techniques lack the AI functionality that ML utilizes. Therefore, MLOps build upon the techniques of DevOps by incorporating AI elements by means of data engineering.

MLOps projects can be undertaken in any number of different spheres as industries are seeing its advantages in many different kinds of applications. Logistics, medicine, transportation, finance, and manufacturing are all using MLOps in their operations, and growth will surely expand to even more industries in the near future.

Steps involved in an MLOps live project

Once you're familiar with the terminology, you’ll be able to better understand the overall procedure. The actual steps in undertaking an MLOps project are the following:

1. Plan and assemble your team.

Before you start, you’ll want to map out your project components and assemble your team. Any given project should include machine learning engineers, data engineers, and DevOps engineers. In addition, you should have a developer and one or more testers in the group.

2. Extract, Transform, and Load

Once you’ve put your plan together and you assemble your team, it’s time to begin your project. The first part will involve extracting all of the data from your database and organizing it efficiently for analysis. For this initiative, you will utilize an API, or an application programming interface.

After this, you’ll need to sort through the data to separate good data from bad. Some data will be useful for your project, and other parts will not be. Remove any duplicates, and load the remaining data into another database. You should automate this process so that future data will go straight into the new database without you having to do it manually.

3. Versioning

Versioning refers to the process of naming discrete versions of ML data as you go through the process of changing them. This is an important step in the process because it allows those undertaking projects to keep close track of all the iterations of their data in case something needs to be recovered.

Careful versioning is critical to the success of any given MLOps project. This is because factors,  including algorithms and parameters, can change as you go about the process. Thus, it can be challenging to determine which datasets belong to a given version unless you keep copies of all of them. You’ll want to ensure the reproducibility of the versions of your data.

Data scientists recommend keeping the following factors in mind during the versioning process:

  • Sort and clear all data
  • Clarify assumptions and lay them out explicitly
  • Conditions should be identical throughout the process
  • Methods should be consistent from version to version
  • Configurations should be consistent for all data scientists in the project

There are tools that data scientists utilize to assist them in the data versioning process, such as KubeFlow and ML Flow.

4. Testing

Once you create and identify your versions, you can begin testing them for validity. You’ll want to validate two different aspects of your results: your models and your data.

Model validation is a way to ensure that the predictive mechanisms of your machine are working effectively and are able to predict future models accurately.

Data validation is a mechanism that ensures new data coming into the system is of good quality. Good data should be distinguishable from bad. Bad data should not be allowed in as it could affect the overall parameters of the operation.

5. Repetition

Having complete data and model validation will put you in a good place with regard to your models. However, it will be worth your while to go back and repeat the process in order to ensure validity. Testing models at different time periods will ensure that nothing has been lost or unduly changed.

In repeating validation procedures, it is important to keep in mind the control variables and make sure the conditions of your operation are consistent with what they were the first time. Any changes could skew the results, which would invalidate your resulting data.

Keep track of your data and models

By following the procedures correctly, you will end up with good results that you will be able to utilize in the future. And once you successfully complete your first project, you’ll be familiar with the procedure. Thus, will be in better shape to conduct similar operations in the future. Keep in mind that precision is the name of the game in MLOps, so the more careful you are about handling your data, the more successful you will be.

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