Deploying ML models in production involves the placement of a working model to a environment where it performs the ML tasks it was built to do.
ML model monitoring and deployment needs quite a bit of documentation, planning, and trial and error, with various tools.
Machine Learning – an Introduction to Model Deployment
Deploying an ML model is a process of placement of a finished ML model to a live environment iin which it is used for the sole purpose it was intended for. ML models are run in wide-ranging environments and often are integrated with the apps via API – to allow access to end users.
Data science development cycle has 4 stages; managing, developing, deploying, and monitoring; the third stage of deployment is kept in mind throughout the process.
Deployment Stage
ML models are built in a testing environment with specifically curated datasets, where these are run for training and testing. Many models built during the development do not fetch desired objectives. Only a limited number of models pass the tests, representing a considerable resource investment.
Thus, deployment in a vigorous environment requires massive preparation and training for the venture to be fruitful.
Deploying ML Models in Production
ML model deployment needs an array of talents and skills working in harmony.
Data science teams develop models, another team works to validate them, and the data engineers are in charge of deploying ML models in production environment.
Preparation of Deploying ML Models in Production
Before deployment the models are required to be trained.
The process involves the selection of an algorithm, tweaking and deciding the parameters, and then running it on clean and prepared data. This entire process takes place in the training environment, and such environments are platforms specially designed for research, along with resources and tools needed for experimenting.
In deployment, the ML models are moved in production settings where the resources are controlled and streamlined for efficient and safer performance.
During the development, work is carried out, and the teams analyze the environment to regulate what applications will have access to the model when finished, along with what type of resources it will require, and how data will be fed to the environment ML models.
Validating Machine Learning Model
After the model is trained with successful results, it should be validated to make sure the one-time/previous success isn’t an anomaly.
The validation process incorporates testing of a model over a new data set while equating results to initial training. Mostly, many models are put through training, yet only a few are effective to pass on to the validation stage.
Of course, from those which are endorsed, typically the most successful model moves on to deployment.
Deployment of ML Models
The deployment process needs multiple different steps and actions; some of the actions are done simultaneously.
The model first should be put into the deployed environment, where it accesses hardware resources it requires and the data foundation it can pull the data from.
The ML model requires to be incorporated into a progression, to make it manageable from the end user’s laptop with API or having it integrated into the end user’s software.
Lastly, people who use the ML model must be trained to run it, accessing the data, while also understanding its output.
Monitoring of ML Models
After the model’s successful deployment comes data science development in the monitoring stage.
Monitoring the model guarantees that the model works appropriately and that its estimates are useful and effective.
Not only the model needs monitoring in initial runs, but the deployment also confirms that supporting resources and software are acting as per requirement, plus end users are trained sufficiently.
Problems that rise after the deployment include the resources being not sufficient, data feeds not being appropriately linked, or the users might not be utilizing the applications appropriately.
When teams determine that ML model and its associating resources work properly, the monitoring process needs to run continuously – but it can be mechanized until a new problem occurs.
Once a team has ensured that the model and its secondary resources are performing appropriately, monitoring still needs to be continued. Still, most of this can be automated until a problem arises.
Deployed models show potential issues over time, such as;
Variance in Data
This is caused when data provided to ML model in deployment isn’t clean the same way like the training and testing data were; this results with changes in deployment.
Data Integrity
With time, variations in data that is being fed to ML models can negatively disturb model performance, like variations in formats, new categories, or renamed fields.
Data Drifting
Market shifts, change in demographics, and other factors cause drifts with time, leaving training data much less applicable to current situations, therefore making the model results less specific.
Final Thoughts
Successful monitoring and deploying ML models in production needs a varied array of skills and partnership between the team. It asks for skill and access to the proper ML tooling and platforms to help teams work efficiently together.
Model oriented organizations deploy models weekly, relying on resources, tooling, and proper platforms – all in the same ML operation platform like Qwak.
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