ML Ops

We need to understand about the Machine Learning model deployment overview even before we get into the issues of machine learning model deployment. Let's start with the same's preliminary steps. 

Data from diverse data sources, data cleansing, feature engineering, and machine learning model training make up the core framework for creating any machine learning model or deep learning model. The model must be deployed in production or made into a product after it is finished.

• Deploy the trained model in production and also test the results.
• Perform continuous model performance monitoring.
• If the model performs below the defined performance then retrain the model, evaluate the model and replace the old model with a new model.
• Model version control and maintaining feature details is a critical parts of model management.
• The model is again deployed in production seamlessly without impacting the existing users using the model.

Underlying model and underlying data are the two key ways that the deployment of an ML model differs from that of a software application. Deployment is nothing more than incorporating the machine learning model into a client's business application, online application, or mobile application. The REST API endpoint for the ML model is made available so that the application may fulfil queries. The deployed machine learning model can provide predictions based on batch data or act as a dynamic model servicing real-time query. Although the deployment of ML models is the penultimate step in the ML lifecycle, it also ushers in the model administration stage. Key participants in the development of ML include data scientists, data engineers, software developers, DevOps, MLOps, and business consultants.

A few of the challenges during ML model deployments are as follows:

• Coordination among the key stakeholders needs a very clear plan and well-thought communication management plan.
• ML models can be built using traditional ML algorithms or DL algorithms. ML models can be developed using umpteen libraries such as TensorFlow or PyTorch or Keras or OpenCV etc., whereas applications such as mobile, web, or business applications are built using Java or C++ or Ruby, etc. Integrating these two will showcase a lot of issues and errors to be handled. While the integration among various software languages is eased to a great extent, it is still considered a daunting task.
• Data drift is a phenomenon where the new data distribution changes over a period of time and reaches a stage where there is going to be a significant change in new data in comparison to the data on which the model was trained. This leads to a reduction in accuracy and is called model drift. Interpretation of results also changes eventually and is called concept drift.
• Deciding whether on-premise deployment has pros that outweigh cloud deployment or not depends on the business problem being solved. It is a decision that is taken after careful evaluation of the pros and cons of on-prem vs cloud deployment and the various factors should be considered including cost, future growth of traffic to the application, real-time vs batch processing, and many other aspects.
• Data Scientists usually assume that once the model is built their responsibility ends there. On the other hand DevOps or Software, developers have no clue whatsoever about what is going on under the hood in the model. They will certainly need more inputs from data scientists to integrate the model with the software application. It is a joint responsibility of both parties.
• Tracking the model performance needs constant monitoring and this cannot be assigned to a senior data scientist it must be assigned as a responsibility to a junior data scientist. Planning on when to update the model and when to retrain and what are the benchmark metrics of accuracy or performance (speed) have to be done at the start of the project.
• Version control will help maintain different versions of ML model releases and a good way to manage is using version control tools such as Git. The various dependency software, libraries, etc., must be managed and tracked throughout. Alongside this securing the model is also extremely important.

The main lesson learned is that model deployment is a challenging and time-consuming job. After setting up the platform for mock model deployment, the model may be trained. The idea of reverse engineering is quite effective.Finally, Model Deployment can be done in various ways and a few of those are listed here.

• Model can be deployed locally on a server or machine
• A model can be saved on a production server for serving the future requests
• A model can be deployed as a REST service
• A model can be deployed as a Managed service

We will talk about ML deployment as a web service in the next post. Please keep an eye on this site, and in the meanwhile, keep developing and learning!

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