Deployment of a machine learning model is the primary part of an ML pipeline and controls the accessibility and applicability of ML models. Making an ML model is one of the key and challenging tasks of building an ML processing pipeline to predict data – however, successfully deployment is crucial, essentially converting your effort and time into real output.
So, exactly how to deploy ML models?
Table of Contents
How to deploy ML models?
At Qwak, we stress several crucial aspects of machine learning model deployment which need attention when thinking about how to deploy ML models.
Data Access and Query
First, you need to ensure that your model has east access to data and can make predictions or/and refrain from doing so itself based on the given data. There are two data querying types for machine learning pipelines:
- Use API to query data which is being stored in another service
- Or use uploaded data provided through a form, from other frameworks or HTML
*Make sure that data remains safe and data transfer is encrypted.
Storage and Data Processing
The vitality of an ML model depends on how the data is stored and processed. Take the example of a CSV; saving a CSV can become a time-consuming and compute-intensive task, or more so when the data files are large.
To curb this, the data can be saved in slices or stored in multiple formats like a binary tree or as a hash table – this way ML model can easily access/process the data without digging through millions of rows of a CSV.
Next on how to deploy ML models – comes the storage of ML Infrastructure.
ML Infrastructure Storage
ML models can be a python file if it is refrained each time, or it can be a pickle file with a loaded python object that is easily loaded and used with incoming data. Simply deployments use the pickle version of trained ML models – still, these are not as common.
*Attain and maintain enough storage space for the pickle files.
Processing Infrastructure
Infrastructure processing is itself a crucial part of deploying an ML model. The processing infrastructure lets the ML model load and is used on incoming data. Build a Flask app with predefined functions to load the ML model while using python. Apply it to an uploaded set of CSV data points. This can also be an API, working every hour to query data from the MongoDB server – looking for new data points and using these to make predictions.
*Debugging is also an important part of processing infrastructure, ensuring that no problem-causing data is loaded onto the model.
Output and Presentation
A workable proper way of displaying the results of the model is important. This can be an HTML file with dynamic variables, allowing the population of accuracy, predicted results, errors, etc. For complicated pipelines, the API makes a result into a PDF or an email, that is forwarded to a pre-specified email ID.
The results can be stored and queried by the user using a key or job ID in some cases.
Results Logging
When we talk about how to deploy ML models, logging of the results comes out as an underrated component of ML models deployment. It is advised to log key stats and results for every run of the ML model, ensuring a smooth run. Logging is also helpful in keeping bugs on track or some of the issues that could not be considered in infrastructure.
Monitoring and Maintenance
Regular maintenance of an ML model is advised yet may not be necessary based on the problem context. For legally regulated important environments such as the loan applications, the ML models need close monitoring with the quick fixing of any arisen biases or drifts. However, in a case where the model has seen all of the population data and does not require retraining – like the biological models of the human DNA – monitoring these is futile, except for finding and fixing errors or bugs.
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Take User Feedback
It is advised to test the models with numerous users before distribution. It is important to collect feedback from the user about their experienced pain points of the model and accordingly address these.
Compute Power & Cloud Infrastructure
It is important to make careful estimates regarding the computing resources and memory required for each job. Cloud infrastructure should then be decided based on this information. A Flask application runs well on a free Heroku server however cannot entertain over 200 users at an instance.
Therefore If you plan on having more users or queries at a time, it is important to go for good AWS servers that offer better performance and memory. Doing so makes the scalability of the model easy.
Final Thoughts on How to Deploy ML Models
When all is done, an ML model’s success depends on multiple factors, including the infrastructure you develop and the one you deploy to run the ML model. Many things can go haywire at the start, so it is important to keep a regular check on your system usage and logs to guarantee a seamless service to the users.
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