DevOps in Machine Learning i.e. MLOps

English Intermediate AI & Machine Learning

The MLOps is a machine Learning Operation intended to automate process of applying life cycle steps of data science, taking machine learning models to production, maintain and monitor them all the time, access their performance, and re-cycle them to re-trained on recent data to align a model for new trends. The ML Ops approach implements Continuous Implementation and Deployment (CI/CD) practices smoothly weaved with monitoring, governance and validations over ML Model life cycle.


Chandrashekhar Deshpande

Microsoft Certified Azure Developer and Trainer

Mr.Chandrashekhar has 16+ years of experience in corporate training and 6+ years of experience in Architecting and Development of Real-life Projects. He is Microsoft Certified Trainer and conducts training on Azure. His expertise lies in training delivery across Big Data, Analytics, Machine Learning, and ML/AI services on Azure. His gamut of specialties is varied ranging from Apache Spark, Analytics and Machine Learning, Apache Kafka, Sqoop, Apache Flink, Apache Flume, Azure HDInsight, Hadoop, and its eco-system including Hive and HBase. Also, he has acquired requisite knowledge of Design Patterns (GoF and JEE), Java Performance Tuning, Java on Azure, Amazon Web Services, Liferay portal, Spring-Liferay integration, and JSE, JEE, Frameworks like Spring, Hibernate/JPA.

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Code of Conduct for local live streams

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A good template can be found at, including internationalized versions at An excellent version of a Code of Conduct, not a template, is built by the DDD Europe conference at