In its earlier years, machine learning (ML) was mostly done for scientific experiments and research. But now, complex ML infrastructures are being considered as the driving force at the heart of many Artificial Intelligence (AI)-driven businesses. Through algorithms, it can increase an application’s ability to predict or make decisions without explicit programming.

The main component of an ML platform is still built on the same fundamental tasks of managing, monitoring, and tracking experiments and models. But to build an infrastructure for machine learning in a production context, there are certain capabilities and considerations needed to be taken into account.

Here’s a brief outline of how you can build a production machine learning infrastructure.

Design Your ML Infrastructure For Production Purposes

Machine Learning for academic purposes is different from industrial machine learning. Software engineers would say that machine learning for academic research is essentially experimentation and processing involving applied mathematics. For the most part, the teaching of machine learning in an academic setting is based on applied optimization theory.

When building your machine learning infrastructure, you should design it according to your production’s needs. Academic ML emphasizes accuracy because it prioritizes the reduction of losses even if it would take a long time to attain accuracy. But industrial ML won’t be able to wait for near-perfect accuracy because the iteration of the model needs to be deployed to production.

Academic ML aims to optimize objective functions, while industrial ML knows you’ll never have absolutely clean objective functions. So industrial ML tries to improve the functions it can and tries as much as possible not to adversely disrupt the other functions. This can be ideal for a production ML infrastructure.

Complete The Critical Elements Of Building Production ML

The second thing you have to do when building your ML infrastructure is to make sure that the capabilities and critical elements for building a production ML are in place. For instance, you should consider leaving enough room for scalability, elasticity, and operationalization of capability. The infrastructure you design should also take into account the need to accelerate ML development once your ML model is deployed to production.

Firstly, your ML infrastructure should have an AI fabric. They should have configuration and orchestration platforms as well, as these will be used to integrate the AI fabric into your machine learning workflows.

Additionally, the ML infrastructure that you’re designing should also include components that will serve as solutions for control of data version, as well as data management. The ML infrastructure should have an ML workbench, a platform where data scientists can have a simple environment to work on their research, train algorithm models, and optimize models and algorithms.

Lastly, your ML infrastructure should have an easy and intuitive way of deploying the ML algorithm model to production and implementation. A lot of the ML models developed weren’t able to make it in production because they found out later on about some hidden technical debt by the organization to a third party.

With that said, you should design your ML infrastructure in such a way that it would be agnostic, meaning it would have no significant technical debt. Otherwise, this could impede its deployment to production and integration into your existing technology stack. It should also be portable and allow simple deployments through the use of containers. Your data scientists should also be enabled to run their experiments and workloads in an easy and simple workflow with just one click.

Optimize ML Server Utilization

You should figure out how to use a visibility tool to track how much of your server capacity is being wasted because of improper or inefficient utilization. Once you’ve done this, your data scientists can use the data and information that they get from this to learn how to improve the way they make use of the ML servers. They can come up with more effective and efficient methods to make use of server capacity and other resources.

That said, here are some of the things that your data scientists can do to optimize ML server utilization:

Stop Jobs That Aren’t Working

Waste of server utilization can happen in data science workflow because it’s still people who make use of ML server resources. What you can do is monitor jobs that aren’t working and stop them right away. There’s a way of visually monitoring idle jobs so you can stop the waste from going on for hours. 

Get Your Insights From The Data

Your data scientists should make the most of your data. They should be able to derive insights about the way machine learning resources are being used by the different users, jobs, containers, etc. From the data, you’ll be able to dive and look into gaps and ways to improve your workflow.

Define Key Questions

It’s important for your organization to properly define key questions for your use case. This can go a long way in fetching the right information that you’re looking for, that could be understood and used by your users and clients. It would also help you find patterns of underperforming workload and users who don’t utilize hardware resources.

Help Your Data Scientists Focus On Development By Minimizing Challenges

Part of the process of building a production ML infrastructure is the continuous development and improvement of the ML algorithms. However, ML engineers and data scientists are often not able to focus on development because they’re not really doing data science work most of the time. This is one of the challenges faced by data scientists in ML infrastructure development today.

Most of the time, data scientists are preoccupied with non-data science tasks. These include configuring hardware such as CPUs and GPUs and configuring orchestration tools in machine learning, as well as containers used for deployment. Instead of working on the development of machine learning models, they’re bogged down by configuration tasks.

Resource management is one of the most challenging aspects of operating and maintaining machine learning infrastructure for production.  There’s a lot of configuration work to be done. If you have a team of five data scientists, it would be quite a challenge to have a GPU on-prem for all five of them. They’d be spending a lot of time trying to figure out how these resources can be managed and shared among them in a simple and efficient manner.

Building ML For Production

Building ML for production is different from building ML for academic and experiment purposes. When building your production ML infrastructure, it’d be good to note that aiming for perfect datasets and models can be a challenge, simply because you’re going to use them in the actual enterprise, business, industrial, or manufacturing operations. Most of the time, these businesses can’t be paused just to wait for your ML infrastructure to be done perfectly.

Oftentimes, you have to build production ML on the fly and hit the ground running as you see whether your iterations are a good fit or not.