Deep learning models are pointless if other people can’t use them.
So how exactly can you deploy machine learning projects to share your ML development prowess with the rest of the world?
In this post, we’ll explore the top tips and processes to consider when deploying successful ML projects, based on the expert insights of our engineers.
This expertise will help your organization better develop intricate models. These models can help to predict, automate, and categorize processes for maximum efficiency. Our insights will help no matter what industry you’re in.
Without further ado, here are three “Dos”, things you must consider and processes you should follow, and three “Don’ts”, things you should avoid, when deploying machine learning projects.
What To Do When Deploying Machine Learning Projects
1. Instrument as much as you can.
ML systems can be affected by a lot of variables and—sometimes—bad predictions. Unfortunately, high CPU and RAM usage are not enough to successfully assess why a system is failing. Therefore, try to instrument your solution as much as you can to increase its level of observability.
2. Be able to scale out.
This is a must for every project in production. You can have the best performing ML model out there, but if your infrastructure can’t keep up with users’ requests, it will be considered a bad implementation.
3. Automate as much as you can.
The nature of every ML project is constantly changing, so it will be a tedious process to manually deploy each one of those iterations. With this in mind, try to automate as much as you can in your ML development cycle. Make sure to also pay special attention to automating the deployment stage specifically.
What You Must Avoid In Machine Learning Projects
1. Treating production as the development environment.
You shouldn’t deploy ML models to production thinking it will only run on the development environment, which is considered to be a controlled environment. In production, you should expect everything—from unusually long inputs to hacking attacks.
2. Don’t version your models.
A machine learning model can degrade its performance for a short period of time. Therefore, you should have a backup in case this happens, which usually entails rolling back to a previous model version.
3. Don’t think about security.
Security tends to be the last thing ML teams think about while developing and deploying a ML product (if they think about it at all!). This is a mistake because ML systems can have a lot of data in them. That data tends to be confidential information. Securing your system must be a priority if you don’t want data leakages or model sabotage.
How Factored Can Help Deploy Your Machine Learning Projects
Deploying successful machine learning projects and models doesn’t have to make you pull your hair out. Armed with the right experts that have experience deploying ML projects, your company can get crucial ML initiatives off the ground and into implementation in no time.
At Factored, our team can bring value by managing the entire ML lifecycle. From developing and delivering ML/AI models, to integrating those models efficiently and continuously with your business so that production can be scaled and you can see prompt results. Our experts can also offer support by building pipelines to automate the process or adjusting or retraining models that have degraded over time.
Since we believe that the proof is in the pudding, here are some machine learning projects that were successfully deployed for our clients:
- We helped a client understand the behavior of a machine learning model to help predict the price movement of stocks and the probability of home equity line of credit default.
- For another client, we used a combination of machine learning and credit risk expertise to build a set of models. This allowed the measurement of credit health of SMEs using only transactional data from their bank accounts.
- We also helped a client by building a natural language processing solution to help them use online customer reviews to determine the strengths and weaknesses of particular services. We built a system connected to a dashboard where all stakeholders could extract insights about the efficiency of their services based on raw text reviews.
Book a meeting with Factored today so we can help you build a team of expert engineers and get your machine learning projects off the ground and deployed in no time.
