5 best practices for deploying Machine Learning models
MLrnmodels today can solve lots of specific business problems across allrnindustries. There have been lots of Machine learning model examples that havernbeen used to solve many business use cases. In this instance, we will look at arnway to create ML models that can be used for production.
Thernproduction process must be streamlined from the beginning to eliminate thernright risks early off.
Therernare many factors to consider when creating a machine-learning ecosystem. Theserninclude data sets, a technology platform, implementation, integration, and thernteams that deploy the ML models. Next comes resilientrntesting to ensure consistent business results.
Thesernare the 5 best practices
1.rnData Assessment
rnDatarnfeasibility must be assessed first. Do we have enough data sets to runrnmachine-learning models? Do we get enough data quickly to make predictions?
Example:rnRestaurant chains (QSRs) can access millions of customers' data. This volumernthorough is sufficient for any ML model that can run on it.
Afterrnminimizing the data risk, it is possible to set up a data lake environment thatrnallows for easy and powerful access from a wide range of data sources. The teamrnwould be able to save a lot of time and bureaucratic overhead by using a datarnlake instead of traditional warehouses.
Thernteam would be able to save a lot of time and bureaucratic overhead by using arndata lake instead of traditional warehouses. A scalable computing environmentrnthat can process the data quickly is also a primary requirement.
Afterrndata scientists have processed, structured, and cleaned up the data, wernrecommend cataloging data for future leveraging.
End-Result:rna well-thought-out governance and security system must be in place to allowrndata sharing among different teams within the organization.
2.rnEvaluation of the best tech stack
rnAfter the MLrnmodels have been chosen, it's important to run them manually to verify theirrnvalidity. In the example of personalized email marketing, is it bringing in newrncustomers, or should we rethink our strategy?
Data science teams should be able to choose from arnvariety of technology stacks in order to experiment and find the one that makesrnML production easier.
Itrnis important to evaluate the technology against stability, business use cases,rnfuture scenarios, cloud readiness, and future scenarios. Gartner projects thatrncloud IaaS will grow at 24% YoY through 2022.
Yourncan watch 1 min video of Mayur Rustagi (CTO & Cofounder - Sigmoid) talkingrnabout the proven methods to approach selecting infrastructure components.
3.rnA robust deployment approach
rnIt isrnstrongly recommended to standardize the deployment process to make integrationrnand testing at different points of the process smooth.
Datarnengineers should concentrate on improving the codebase and integrating thernmodel (as API endpoints or bulk process models), and creating workflowrnautomation like smooth ML pipeline architecture to allow teams to integraterneasily.
Forrnany ML model to succeed, you must have access to the correct datasets andrnmodels.
4.rnPost deployment support & testing
rnIf you havernthe right tools to log, monitor, and report the results, it will make testing arnmuch easier process.
ThernML environment must be evaluated in real-time and closely monitored. The datarnengineering team should receive test results so that they can update thernmodels.
Datarnengineers might decide to overweight the high-performing variants andrnunderweight the weaker ones.
Yournshould be aware of any negative or unexpected results. It is important to meetrnthe right SLAs. Monitoring should be done to ensure that data quality and modelrnperformance is maintained.
Thisrnwould lead to a steady stabilization of the production environment.
5.rnCommunication and change management
rnClearrncommunication between cross-functional teams is crucial for ML models' success.rnThis ensures that all risks are managed at the right time.
Data engineering and data scientists must collaboraternto produce an ML model. Data scientists should have complete control of thernsystem to see production results and check in code. Sometimes, teams may needrnto be trained for new environments.
Transparencyrnin communication will save everyone time and effort.
Conclusion:
Inrnaddition to all the above best practices, the machine learning model must bernflexible and adaptable to drastic changes. It is best to not have all of thernrecommended methods but to make certain areas mature enough and scalable tornallow them to be calibrated as required by the business.