Hello and Welcome from 360DigiTMG. This is a video playlist to explain the life cycle of a data science project. In the previous video we talked about Model Evaluation. In this video let us discuss the final step in the data science life cycle – Model Deployment and Monitoring.


Model Deployment and Monitoring: This is the final step of the data science life cycle. After a model is trained, tuned, and tested, you can deploy the model into production, and make inferences (predictions) against the model. There are a multitude of ways in which you could deploy your model into production, it could be done on the cloud (AWS, GCP, Azure etc) or on-premise hardware. Since the model is already trained, you may not need to have a heavy instance for inference on new (unseen) data to make predictions. This may not be true for models that need to be continuously retrained (like real-time streaming data). Always consider building feature data processing and feature engineering pipelines with a suite of feature transformers readily available in the SparkML and/or Scikit-learn framework containers, and deploy these as part of the inference pipelines to reuse data processing code and simplify management of your Machine Learning processes. After deployment, continuously monitor the models. After an Machine Learning model is deployed in production, the real-world data might start to differ from the data that was used to train the model, leading to deviations in model quality, and eventually less accurate models. Build a Model Monitor that detects deviations, such as data drift, that can degrade model performance over time, and alerts you to take remedial actions.

Best Practices while deploying models into production:

• Monitor model performance in production and compare to business expectations
• Monitor differences between model performance during training and in production
• When changes in model performance are detected, retrain the model. For example, sales expectations and subsequent predictions may change due to new competition

So, that completes our walk through of the data science life cycle. Hope you enjoyed this video! See you in the next video!



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360digiTMG is a 5-year-old training & consulting organization led by stalwarts of the industry who are alumnus of premier institutions like the Indian Institute of Technology, Indian Institute of Management and Indian School of Business. 360digiTMG since its inception has been the forerunner in the space of management and niche programs that aid in up-skilling and cross skilling executives across various levels and domains. 360digiTMG has been conducting training programs across the globe for corporate and individuals alike.
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