Machine Learning Workflow And Its Methods Of Deployment

This article discusses machine learning, its various components, and dives deep into the deployment of machine learning models into production. Different paths of deployment are explored with each described in detail. We also learn about PMML and PFA and about the importance of learning deployment of machine learning models.

Machine learning 

Machine Learning refers to the process by which machines can be taught to learn from data. It is the approach of teaching computer models to learn from data to make predictions and draw out conclusions. Usually, huge data is needed to create these models and to train them to design an effective and accurate system. 

The primary components of Machine Learning Workflow can be listed as follows,

Exploration and Processing of Data

The data can be raw, structured, and unstructured. It can be in any form as long as; the data are of high quality to work upon. In the exploration and preparation phase, the data are cleaned and wrangled and set up for the modeling. Retrieving the data, Cleaning and Exploring the data, and Preparation and Transformation are all included in this phase.

Modeling

The models are trained using various algorithms as best suited for the purpose of usage. Thereafter, the model is tested and evaluated and the process of training and testing is iterated to produce a golden model. With this, a proper higher prediction model is produced for use. Here, the Development and Training of Models occur and the validation and evaluation of the model take place.

Deployment

Deployment to production can be understood as the process of integrating machine learning models into a production environment such that decisions and predictions can be made based on the input data of the model. While moving from the modeling phase to the deployment, the model is supposed to be provided to those who are responsible for the process of deployment. The deployment to production and continuous monitoring and updating of models and data occurs in this phase. Here, in this article, we assume, we are taking an application coded in python and deploying it and thus, the examples to follow will take the model being developed in Python.

Machine Learning Workflow and its Methods of Deployment

Production Code 

The production code is basically the software that runs on the production servers. These codes are responsible for everything that happens on the internet for the system which handles data and every interaction with the live users online. 

Production Environment 

Known as Production or the Production Environment, it refers to the location where the application is live for the public and the intended users. Bugs should have been fixed before launching the system to the production maintaining all the necessary requirements of coding standards and practices on the production code.

Different Deployment Paths

There are numerous ways to deploy models from the modeling component of the machine learning workflow to its deployment component. Starting with the least commonly used ways, here are the methods.

  1. Recoding Python Model into the programming language of the production environment.
  2. Coding the Model into Portable Format Analytics (PFA) or Predictive Model Markup Language (PMML) 
  3. Conversion of the Python Model into the format which can be used in the production environment.

Recoding Python Model into the programming language of the production environment 

This is the least likely used method in the present time. It involves the process of recording the entire Model coded in Python to be converted into another language for the production environment such as C++ or Java. Rarely used anymore, we have come up with better ways of other methods for deployment. This process takes much more time to recode the models, test, and validate them such that it provides similar predictions as the original model.

Coding the Model into Portable Format Analytics (PFA) or Predictive Model Markup Language (PMML) 

This method is the approach of coding the model into Portable Format Analytics (PFA) or Predictive Model Markup Language (PMML) such that the standards are maintained in order to move the predictive models to the deployment is simplified for a production environment. Both PMML and PFA provide a vendor neutral executable model specification in order to produce predictive models that are frequently used in machine learning as well as numerous aspects of data mining. Various analytic software such as R, Apache Spark, Teradata Warehouse Miner, IMB SPSS, SAS Base and Enterprise Miner, TIBCO Spotfire, and many more allow the direct import of PMML.

Conversion of the Python Model into the format which can be used in the production environment 

This is the industry standard as of today and is used across the globe for the numerous this method provides. A model built in Python can use various methods and libraries which can easily convert the model into code which are used in the production environment. Numerous machine learning software frameworks such as TensorFlow, PyTorch, SciKit-Learn all enable the use of methods that can convert the models written in Python into an intermediate standard format such as Open Neural Network Exchange (ONNX). These intermediate standard formats can be easily converted for the production environment in their respective software native format.

Some of the benefits of using this conversion approach are as follows,

  • Fastest and the easiest method to move the models developed in Python to the production environment.
  • It is the future of moving models of Machine Learning applications to the production environment and being used to this system will help you as a developer and dev-ops down the lane with an acquired experience and skillset.
  • Various other technologies such as endpoints and APIs, containers too contribute to this process of easing the deployment models into production environments.

Predictive Model Markup Language (PMML)

Both PMML and PFA were developed by the Data Mining Group. The PMML focuses on the standards for various statistical and data mining models.

Portable Format Analytics (PFA)

PFA is based on JSON and provides a method to exchange and describe predictive models for analytic applications. 

Why Learning about Deployment is important

Across multitudes of learning platforms for Machine Learning, while considering the components of the workflow for Machine Learning, only Exploration and Processing of Data are explored with Modeling. One of the reasons is that modeling and data are tightly coupled with each other. It is a no brainer that modeling cannot take place without appropriate data. Howsoever, learning about the deployment of these models to the production environment is of equal importance without which the works we do can never reach out to the global audience. Previously, data exploration, processing, and modeling were handled by analysts and the deployment for production environment by the software developers. Recent developments have bridged that gap as the division between the operation and development has softened such that analysts too can handle parts of the deployment which will help create faster updates to the models and better it over time.

Deployment is key to Machine Learning and just like exploring and processing data and building models, testing and validating it, Deployment too should be learned. Advanced Cloud Services such as ML Engine – Vertex AI, Amazon SageMaker, and Microsoft Azure each provide equally promising functionalities and features to develop, build and deploy Machine Learning Applications. Read the previous articles, Azure Machine Learning Pipelines, Microsoft Azure AI Fundamentals, and Auto ML to learn more.

Conclusion

Thus, we learned about machine learning, its various primary components, and the different ways machine learning can be deployed in production. Later we learned about Predictive Model Markup Language, Portable Format Analytics and learned about the importance of learning deployment of machine learning models.