7 Tips for Choosing the Right Machine Learning Infrastructure

Machine learning (ML) is an exciting but often challenging field. Training these intelligent models requires 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 will 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 software. That’s a lot to consider, so here are seven tips to help you choose the right ingredients 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. One-third of all ML projects stop at the proof-of-concept stage, more than any other stage, but if you outline your specific goals from the start, you’ll find it easier to come up with a relevant, effective plan.

Ask why you want to build a machine learning model, where you will use it, how you will 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 components for your ML infrastructure.

“One-third of all PL projects stop at the proof-of-concept stage.”

2. Outline your needs

Once you know your goals, you need to outline your needs. These are the constraints you face that can limit your options for achieving your goals. Creating a specific list of these requirements will help avoid headaches later in development.

Your budget is one of the most important requirements, as new technologies often have high upfront costs and a slow return on investment (ROI). Other things to consider are your computing power needs, other data storage you’ll need, and how much data you think you can reasonably collect to train the model.

3. Consider the format of your data

You probably already know that you need a lot of data to build an effective ML model. However, when choosing your ML infrastructure, it’s easy to overlook the types of data you need. Depending on what kind of system you’re building, you may need plain text, images, video, or a variety of file types, all of which have unique processing needs.

Video and image files will take up much more space than text, so you need more storage. You’ll also need software that supports the types of files you plan to collect. Be sure to be as thorough as possible here, as there can be significant differences even in the same type 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. Lack of relevant skills is the most common challenge businesses face in AI projects, but you can address it by targeting accessibility from the start.

Instead of finding the right people to work on a complex machine learning system, try building an ML pipeline that’s simple enough for you to manage right now. The better fit all of your components are, the better you’ll be able to achieve 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, build an ML pipeline that’s simple enough for you to manage right now.”

5. Consider scalability

Likewise, you need to consider how scalable your machine learning infrastructure needs to be. Projects like this usually work best when you start small and grow from there;

How scalable you should aim for depends on your project goals, how much you think your ML investment will grow, and your budget options. However, in general, it is better 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 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 40-60 software tools, but only uses 45% of them. Take the time to consolidate applications where you can, and look for a 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 neglect safety

Cyber ​​security is another important part of choosing the right machine learning infrastructure. Training and deploying a machine learning model means storing a lot of data in one place, which can make you a valuable target for cybercriminals. Considering how 63% of organizations will experience a data breach in 2021, costing an average of $2.4 million, locking down this data is critical.

Look for ML tools with strong built-in protection. 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 budget for any new cybersecurity tools you may need, as the new software you implement may have different security requirements.

Find your ideal machine learning infrastructure

Your ML infrastructure significantly impacts the cost, efficiency, and ROI of your machine learning project. If you want to build a successful ML application, you should carefully consider these tools.

Following these seven steps will help you find the right hardware and software for your needs. When you do, you can fully experience machine learning.

Also read 8 Ways Machine Learning Will Impact Education

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