Machine Learning Model Management

The tools used in the development cycle for Machine Learning and the managing of the models require MLOps - Machine Learning Operations.

Machine Learning Model Management
Alvaro Reyes via Unsplash


When you think of Machine Learning, you think about models. These models need effective management to ensure that they are producing the outputs required to solve a specific problem or task. 

Machine Learning Model Management is used to help Data Scientists, Machine Learning engineers, and more to keep track and on top of all their experiments and the results produced by the model. Machine Learning Model Management sole responsibility is ensuring that the development, training, versioning and deployment of ML models is managed at an effective level. 

The tools used in the development cycle for Machine Learning and the managing of the models require MLOps - Machine Learning Operations. 

To recap and for those of you who may be unsure, MLOps is a core function to the engineering of Machine Learning. It better helps organizations manage the machine learning lifecycle through automation and scalability by using practices such as collaborations and communication between the team. 

MLOps improves:

  • Collaboration between members of the data team
  • Automating repetitive tasks, reducing the need for human input
  • Improving models by inputting new data
  • Using customer knowledge and identifying patterns to improve the overall experience 

These practices improve the overall machine learning lifecycle, the management process, as well as scalability. 


The Layers of ML Model Management


Prior to managing models, the lifecycle consists of preparing data, the engineering of features to then train, build, and test the model before the best performing model goes for deployment. It is then at this point when ML Model Management is used. This is where the model is evaluated, compared, rebuilt and monitored.

The ML Model Management can be split up into two sections. The first is experiment tracking and the other is the versioning and deployment of the model. 


Experiment Tracking


This consists of training the model, evaluating the model and then reviewing the model's architecture again, and again. 


Model Versioning and Deployment


Versioning is when you essentially create a new version of the model with implemented changes, which then the model goes on to get deployed. 

Until the best performing model enters the deployment phase, this process continues along with the prior phases on engineering features, data labeling, and data versioning. 

Data Scientists use ML Model Management to oversee: 

  1. The Packaging of the Model - this is the exporting of the final model in a particular format
  2. Model History - this is everything related to the creation of the model, such as the data used, the parameters, its training, etc. 
  3. Deployment of the Model - this process helps Data Scientists in their decision making process
  4. Monitoring the Model - this is where Data Scientists will track and monitor the performance and accuracy of the Model
  5. Retraining - this is an important step to continue to improve the performance of the model with new data, etc. 


Why Do We Need ML Model Management?


It would take a lot of time and money for Data Scientists to continuously build, track, compare, re-build and deploy models. It has been done before, and that’s why there is ML Model Management. 

ML Model Management makes it much easier to manage the lifecycle of a ML model through experimentation and understanding the model better. It has helped teams be more efficient and steer them into the right direction of which areas to improve; allowing them for better research and overall productive development. 

Collaboration is a big element to the MLOps pipeline allowing for different members of the team to understand the problem at hand before any steps of the life cycle have occurred, making comments, using prior experiment as a baseline, and the overall reviewing of the entire lifecycle. This can involve Data Scientists, Researchers, and ML Engineers to all work collaboratively and improve the overall lifecycle. 


How to Implement Machine Learning Model Management


So we understand that our ML Model Management will consist of data labeling, data versioning, experiment tracking of the model, model versioning and model deployment. 

Experiment tracking is an important element that needs to be implemented to ensure the overall ML Model Management is working effectively. It consists of  collecting, organizing, and tracking the model providing further information such as the size of the model, the parameters used, and more. Below are the main 3 ways on how you can implement Experiment tracking in your ML model management.




As we mentioned, keeping track of experiments and the results produced is very unorganized and time-consuming - especially when you have a number of people working on the same project.  This is where logging is important and can also be replicated for future use; which again saves a lot of time and money. The parameters which will be important to log are:

  • The name of the model 
  • The version number of the model
  • The parameters used in each iteration during the training process
  • The training accuracy during each iteration of the training process
  • Training time
  • The test accuracy 
  • A confusion matrix 
  • Memory consumption 
  • The probabilities of each model based in the data inputted
  • Code
  • Environment configurations
  • Problems and potential solutions
  • Evaluation of the solutions


Version Control


There will be a lot of versioning happening during the ML Model lifecycle, therefore it is important to track and manage all these changes to give you a better understanding of which model is the best performing. 




A dashboard is the biggest driver for collaboration. Data Scientists and researchers can then use this to better understand the model and experiments, investigate it, review it better and share their findings with other collaborators for further review. 

The dashboard will contain all the information regarding the experiments, metadata, and more. It helps you to visualize through graphs of everything that has been logged and versioned data to help you better compare and discover differences.


Wrapping It Up


Machine Learning Model Management is making a lot of Data Scientists and Researchers life easier. It is an important element to the MLOps workflow; allowing for better team collaboration, deeper insights, opportunity to review as a team, productivity, time efficient and improving the overall lifecycle. 

Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.