Managing ML Model

Top Things to Know Before Managing the End-to-End Lifecycle of Your ML Model

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Managing your ML models is vital for businesses that use machine learning. Ensuring proper management of ML models throughout their entire lifecycle is critical. It makes it easy to derive practical business value from them and take your business to the next level.

However, managing machine learning models isn’t as straightforward as you’d think. At least not for a starter who doesn’t know the nitty gritty of machine learning models. Also, it requires you to have a proper understanding of the machine learning lifecycle and what happens in it.

This article will help make machine learning management easier for you. It will discuss all you need to know, including the ML model end-to-end cycle and its steps.

Let’s get started.

Understanding the Machine Learning Lifecycle

It is vital to start by knowing what the machine learning lifecycle is. The ML lifecycle is a cyclical process that data science projects like ML models follow to be productive. It consists of various steps that are worth learning if you use machine learning technology in your organization.

In managing the ML model, a relevant tool is the model registry. You need it to

  • Effortlessly manage the ML model lifecycle
  • Enable easy collaboration between teams
  • Roll-out products faster and seamlessly
  • Approve workflows and model risks

In short, a model registry is essential for the model lifecycle. The model lineage found in it provides information that helps in the maintenance of models. It is vital to keep your model registry updated by adding new models to it after developing and deploying them.

A model registry is a database that can be as big as you want. You can expand it whenever you scale your organization to include all your models. But, your success with model management will depend on how you manage the process.

Ensure you do not skip a step and always follow the correct order. That will help ensure you get the desired results in the end and also increase the lifespan of your ML models.

Steps Involved in Machine Learning Lifecycle

Most people think that model management should start after deploying your models. However, the reality is that model management is a process that begins early on when you’re building your models. Here are the steps involved in the machine learning lifecycle:

1. Building Your ML Models

This is the first step of building models for a business. It is also the step where ML model management should start. Building ML models that meet your company’s needs won’t be easy if you do not manage the development process properly. Starting model management at this stage increases efficiency.

2. Data Preparation

Another step of this process is the preparation of data for model development. The data you input can come from different sources, with some of it coming in during the model’s learning stages. Manage data preparation to ensure you feed it with data that can deliver the desired output.

3. Model Training

This is where you create a model out of the data collected. Part of the data helps you find model parameters, while the other enables you to test the model. But then, it is possible to get everything wrong if you do not take your time to manage the data used for model training well.

4. Parameter Selection

You should also choose the parameters that will help control the effectiveness of the model training process. The ultimate performance of the models you’ll deploy will depend on how well you select parameters. This is a crucial area where model management can be helpful to you.

5. Transfer Learning

You may want to reuse models in more than one domain, which isn’t possible. What you can do is use one ML model to train another. This can help you significantly cut the time you’d have spent training models. ML management ensures you transfer the data you use correctly.

6. Model Verification

First, it is vital to confirm that the model you have provides sufficient output for users. Model verification ensures that you have a good view of the value you’ll get from using an ML model. It is vital to ensure smooth management of this step because you need an accurate picture of your models.

7. Deploy the Machine Learning Model

Integrating ML models with processes and applications is a vital step. It is where a business starts to enjoy the full benefits of its models. Proper functionality is the ultimate aim in this step, and the best way to ensure it is by managing your models properly.

8. Monitoring

You need to add proper safety measures to your application for the best results. This should happen throughout its entire lifespan. Also, you need to constantly update it and manage it as expected since that’s the only way to ensure you always get the desired results from a model.

Using MLOps in Managing the Lifecycle of an ML Model

Image of Using MLOPs in Managing ML Model

Machine learning operations (MLOPs) are practices, principles, and technologies used by DevOps teams. It is critical for ensuring there’s increased efficiency of machine learning workflows. The whole process of ML model management is part of this broader MLOPs framework.

MLOPs can be critical to the management of the machine learning lifecycle. It can speed up the steps of the model management process discussed above and ensure everything runs smoothly.

In the end, vital tasks like experimentation and collaboration become easy, ensuring you get the desired value. It also makes it easy for your ML models to remain productive throughout their lifespan.


You now know all you need to before starting to manage the end-to-end ML model lifecycle. The tips shared in this article will be helpful for the much-needed ML model management. You can ensure the entire ML model lifecycle runs smoothly from the start to the end.

We have discussed the steps involved in the machine learning lifecycle. Managing your ML models in every step of the lifecycle discussed above is vital. You also should ensure that you use the correct set of tools to manage your ML models and get the best results.

Also Read: Common Challenges In Adopting Machine Learning For Business



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