One of the biggest problems with machine learning is how to deploy it into production. Deploying your machine learning model into a production program is often one of the most challenging and time-consuming processes practitioners have to go through. It is why your deployment strategy is a crucial component of the machine learning implementation process. However, before you can begin formulating a strategy, you have to answer a few important questions about how end users will interact with your model predictions, and you must also know how frequently you will have to generate predictions. On top of that, you should also know technical details like the latency requirements and how many applications will have to access your model. Knowing the answers to these questions is vital to reduce the complexity that comes from the process of creating, training, deploying, and maintaining your machine learning models.
How Machine Learning Systems Compare to Traditional Software Deployments
What makes machine learning deployments so different from traditional software deployment is the ML model you need to take care of. For ML systems, you need to deploy a trained model along with your software code. The majority of your program will come from that model that you have deployed. The model has to be trained, retrained, and you need to validate it while you are deploying as well. All of these things add extra complexity when compared to traditional software deployment. Traditional software deployment usually involves compiling and deploying code to some platform. You might need graphics or other elements, but it is often not as difficult as machine learning deployments.
Making Models Portable
Another issue you need to work around with machine learning deployments is model portability. Most data scientists work in an online Jupyter notebook, but you cannot easily take this system and transport it to a production environment. You need to convert your code to an intermediate format that is language agnostic and lightweight. It is one of the biggest challenges that machine learning practitioners have to contend with. However, there are a few formats currently in use, including PFA, Pickle, and NNEF.
Using CI/CD in Your Machine Learning Systems
Software deployment also benefits from using continuous integration and deployment. There is no such system for many machine learning solutions, as it involves deploying data, model, and code together into a single unit. Software only needs to deploy code, making it easy for the continuous integration and deployment software to do its job. With machine learning systems, each time you update your code or model, you need to go through the deployment process again. This is where MLOps is quickly becoming a crucial component of machine learning model deployment. MLOps provides the tools and functionality that you would get from DevOps in software engineering.
Techniques for Deploying Your Models
There are a few ways of deploying your machine learning model to production. For example, you could have periodic updates of your model every few weeks or even do real-time updates to the model. The option you choose will often dictate how complicated your machine learning program is. Most companies use a web service approach. This means that the model is held on its own server, and you simply query the server for information when you get a request for model information. By doing things this way, the model web service acts as a microservice that you can update whenever you want.
Improving Your Performance
Performance is another major factor in all of this. It requires that you do careful monitoring to ensure that your model is accurate throughout its lifecycle. Like everything in life, models will eventually get old and stale. You want to ensure that you keep it updated to provide the most accurate results possible.
Putting your machine learning model into production is a difficult process. However, new tools and methodologies are being introduced every day to help you get a handle on things.