Machine learning (ML) is an exciting but often challenging field. Training these intelligent models takes a lot of work and the right mix of other software and hardware. If you want to make the most of this technology, you need to choose your machine-learning infrastructure carefully.
This infrastructure covers all the hardware and software tools you’ll use to train and deploy your ML models. That includes ML frameworks, data storage technologies, testing tools, security software and devices to run all these programs on. That’s a lot to consider, so here are seven tips to help you choose the right components for your needs.
1. Determine Your Goals
The first step in choosing a machine learning infrastructure is deciding what you want from your machine learning models. A third of all ML projects stall in the proof of concept stage — more than any other phase — but if you outline your specific goals from the beginning, you’ll have an easier time putting together a relevant, effective plan.
Ask why you want to build a machine learning model, where you’ll apply it, how you’ll use it and what benefits you expect to get from it. The answers to these questions should guide every other decision you make when selecting ML infrastructure components.
“A third of all ML projects stall in the proof of concept stage.”
2. Outline Your Needs
Once you know your goals, you should outline your needs. These are the restrictions you face that may limit your options for meeting your goals. Creating a specific list of these requirements will help avoid getting in over your head later in development.
Your budget is one of the most important requirements, as new technologies often have high upfront costs and slow returns on investment (ROIs). Other things to consider are your compute power needs, any more data storage you’ll require and how much data you think you can reasonably gather to train the model.
3. Consider Your Data Format
You probably already know you’ll need a lot of data to build an effective ML model. However, it’s easy to overlook the kind of data you need when choosing your ML infrastructure. Depending on what kind of system you’re making, you may need plain text, images, videos or a range of multiple file types, and all of these carry unique processing needs.
Video and image files will take up much more space than text, so you’ll need more storage. You’ll also need software that supports the file types you plan on collecting. Be sure to be as granular as possible here, as there can be significant differences even in the same kind of data. JPEGs and PNGs are both images, but JPEGs are smaller in size and PNGs retain quality better when compressed.
4. Aim for Accessibility
Another important thing to keep in mind is how easy your infrastructure is to use. A lack of relevant skills is the most common challenge businesses face in AI projects, but you can address that by aiming for accessibility from the start.
Instead of trying to find the right people to handle a complex machine learning system, try to make an ML pipeline that’s simple enough for you to manage right now. The more user-friendly all your components are, the better you’ll be able to meet your goals and the faster you’ll see a positive ROI.
“Instead of trying to find the right people to handle a complex machine learning system, make an ML pipeline that’s simple enough for you to manage right now.”
5. Keep Scalability in Mind
Similarly, you should consider how scalable your machine-learning infrastructure needs to be. Projects like this typically work best when you start small and grow from there — to do that, you’ll need infrastructure that’s easier and more affordable to scale up.
How much scalability you should aim for depends on your project goals, how much you think your ML investments will grow and your budget options. Generally speaking, though, it’s best to use a cloud-based solution for data storage and ML pipelines, and the cloud is more cost-effective than on-premise hardware when scaling.
6. Look for Interoperability
A great way to keep things scalable and affordable is to look for solutions that fit with the hardware and software you already use. If you can get tools that work with your current setup instead of replacing everything, you can save a lot of time and money.
The average company already has 40 to 60 software tools but only uses 45% of them. Take the time to consolidate apps where you can and look for machine learning infrastructure that works with these tools to minimize IT sprawl.
“If you can get tools that work with your current setup instead of replacing everything, you can save a lot of time and money.”
7. Don’t Overlook Security
Cybersecurity is another crucial part of choosing the proper machine learning infrastructure. Training and deploying a machine learning model means keeping a lot of data in one place, which can make you a valuable target for cybercriminals. Considering how 63% of organizations in 2021 experienced a data breach, costing $2.4 million on average, locking this data down is essential.
Look for ML tools with strong built-in protections. It’s also a good idea to look for things that are compatible with your current security software. Be sure to set aside some of your budgets for any new cybersecurity tools you may need, as the new software you implement may come with different security requirements.
Find Your Ideal Machine Learning Infrastructure
Your ML infrastructure significantly impacts your machine learning project’s costs, effectiveness and ROI. If you want to create a successful ML application, you need to consider these tools carefully.
Following these seven steps will help you find the right hardware and software for your needs. When you do that, you can experience machine learning to its fullest.