Best Practices for Integrating ML in Your Enterprise

In the present times, Machine Learning (ML) models help solve a wide array of very specific business challenges faced by organizations across industries. Hence, it is important to choose an ML model that aligns with the problem you need to solve. Finally, the approach you choose to build your ML model must be productionable and easily executable for long-term results.

Check out this infographic that highlights the best practices that you can follow when integrating ML models with your business applications.

Best Machine Learning Practices for the Cloud

Cloud or On-Premise?

Choosing the cloud means saving time, easy scaling, and low entry costs. Moreover, there are big players such as Microsoft Azure providing services that allow for easy scaling.

Use Checkpoints

Having checkpoints after every training and evaluation stage using parameters and hyperparameters can help immensely.

Gather Historical Data from the Current Systems

Gathering already existing data can help you understand where optimization is necessary to give you the best results.

Create a Business Objective & Problem Statement

Having an objective or a problem statement helps prioritize. It is a metric that you can optimize and will show you progress.

Use Easily Attributable & Observable Metrics

Using simple metrics, in the beginning, is a great idea. While indirect effects offer great added value in the long run, it is better to start small.

Pass Sanity Checks Before Deploying The Final Model

Testing is the key before you deploy your machine learning model. You can even automate the process and check the generated metrics later to see if it gives you the expected results. Some standard metrics you can deploy are f1 score, accuracy, or recall.

Use Kubernetes & Containers in Deployment

Technologies such as Kubernetes and Docker help you encapsulate different parts of the system. That helps in making incremental improvements, and the scaling process becomes easier.

Takeaway

Deep Learning and Machine Learning have come a long way from being mere buzzwords. Today, they are an integral part of startups and businesses worldwide. Still, many organizations find ML integration to be challenging. However, following these best practices can help them take off in the right direction to eliminate the risks early in the production process.

Sources Used

https://www.forbes.com/sites/forbestechcouncil/2019/08/15/four-best-practices-for-using-machine-learning-in-your-product-or-platform/?sh=4576f55b3ae4

https://towardsdatascience.com/mlops-practices-for-data-scientists-dbb01be45dd8

https://www.cio.com/article/3623353/best-practices-to-embrace-an-mlops-mindset.html

https://medium.com/@ODSC/best-practices-for-deploying-machine-learning-in-the-enterprise-35a1907203d4