ML models today can solve lots of specific business problems across all industries. There have been lots of Machine learning model examples that have been used to solve many business use cases. In this instance, we will look at a way to create ML models that can be used for production.
The production process must be streamlined from the beginning to eliminate the right risks early off.
There are many factors to consider when creating a machine-learning ecosystem. These include data sets, a technology platform, implementation, integration, and the teams that deploy the ML models. Next comes resilient testing to ensure consistent business results.
These are the 5 best practices
- Data Assessment
Data feasibility must be assessed first. Do we have enough data sets to run machine-learning models? Do we get enough data quickly to make predictions?
Example: Restaurant chains (QSRs) can access millions of customers’ data. This volume thorough is sufficient for any ML model that can run on it.
After minimizing the data risk, it is possible to set up a data lake environment that allows for easy and powerful access from a wide range of data sources. The team would be able to save a lot of time and bureaucratic overhead by using a data lake instead of traditional warehouses.
The team would be able to save a lot of time and bureaucratic overhead by using a data lake instead of traditional warehouses. A scalable computing environment that can process the data quickly is also a primary requirement.
After data scientists have processed, structured, and cleaned up the data, we recommend cataloging data for future leveraging.
End-Result: a well-thought-out governance and security system must be in place to allow data sharing among different teams within the organization.
- Evaluation of the best tech stack
After the ML models have been chosen, it’s important to run them manually to verify their validity. In the example of personalized email marketing, is it bringing in new customers, or should we rethink our strategy?
Data science teams should be able to choose from a variety of technology stacks in order to experiment and find the one that makes ML production easier.
It is important to evaluate the technology against stability, business use cases, future scenarios, cloud readiness, and future scenarios. Gartner projects that cloud IaaS will grow at 24% YoY through 2022.
You can watch 1 min video of Mayur Rustagi (CTO & Cofounder - Sigmoid) talking about the proven methods to approach selecting infrastructure components
- A robust deployment approach
It is strongly recommended to standardize the deployment process to make integration and testing at different points of the process smooth.
Data engineers should concentrate on improving the codebase and integrating the model (as API endpoints or bulk process models), and creating workflow automation like smooth ML pipeline architecture to allow teams to integrate easily.
For any ML model to succeed, you must have access to the correct datasets and models.
- Post-deployment support & testing
If you have the right tools to log, monitor, and report the results, it will make testing a much easier process.
The ML environment must be evaluated in real-time and closely monitored. The data engineering team should receive test results so that they can update the models.
Data engineers might decide to overweight the high-performing variants and underweight the weaker ones.
You should be aware of any negative or unexpected results. It is important to meet the right SLAs. Monitoring should be done to ensure that data quality and model performance is maintained.
This would lead to a steady stabilization of the production environment.
- Communication and change management
Clear communication between cross-functional teams is crucial for ML models’ success. This ensures that all risks are managed at the right time.
Data engineering and data scientists must collaborate to produce an ML model. Data scientists should have complete control of the system to see production results and check in code. Sometimes, teams may need to be trained for new environments.
Transparency in communication will save everyone time and effort.
In addition to all the above best practices, the machine learning model must be flexible and adaptable to drastic changes. It is best to not have all of the recommended methods but to make certain areas mature enough and scalable to allow them to be calibrated as required by the business.