How do you deploy Machine Learning models on the cloud?

Anton Knight
Anton KnightAnswered

The high demand for Machine Learning development in recent years influenced a high demand in job roles concerning it, growing day by day  knowing that the experts found a way to create, train, and develop more complex Machine Learning models thanks to the incorporation of Cloud services. Deploying models into production simply means incorporating the new model into an already created production environment, where the model predicts the output based on the received input(/s). It can be done in different environments and be integrated with various mobile and web applications, thanks to the API. Cloud solutions are a great choice for Machine Learning development processing. Some of the benefits include:

  • Cost-effectiveness, for businesses and individuals.
  • Higher security level, keeping information safe and preventing a data breach
  • Available on various devices, individual or company employees can access the file on various devices (tablets, laptops, desktop computers)
  • Using 3rd party services and customizing them to fit model needs

There are various Cloud providers available on the market, but the dominant few are IBM Cloud, AWS (Amazon Web Services), Google Cloud, and Azure. All of them have sophisticated options and services to make Machine Learning development a comfortable and efficient experience regardless of user type (business or individual). Deploy Machine Learning model to production became easier and unified on a basic level. Some basic steps common for all Cloud services that support AI and Machine Learning development are:

  1. Creating an account with a Cloud Service provider
  2. Prepare the data
  3. Create a project
  4. Write a code in a notebook
  5. Train and evaluate your model
  6. Tune it and rerun.
  7. Deploy your model and evaluate acquired predictions.It is advisable to deploy different versions of your model and validate your outcomes.
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