How to get started with AI and Deep Learning in Enterprise 960 x 610

Automation is undeniably the foremost trend that emerged during the pandemic. For the most part, this is thanks to the increasing sophistication of machine learning — and an even more advanced subset known as deep learning.

Deep learning is unique for its ability to help computers learn without needing to be programmed beforehand. This technology is highly versatile and currently has use cases ranging from corporate to industrial settings. We are using NeoPulse AI Studio for drug discovery, medical diagnosis, and disease management. Manufacturers are using it for inventory prediction and management.

With the market developing at a rapid rate, there’s currently an increased demand for machine learning and deep learning engineers. According to Brandon Purell, Senior Analyst at Forrester Research, this is because “one hundred percent of any company’s future success depends on adopting machine learning”. In turn, this has led to a rapid expansion of software development programs at universities that train these new data scientists. An example of this is how modern software and IT degrees have become available online at all levels of higher education. An online master’s in software development allows students looking to go into more specialized areas, such as deep learning, to study at top universities without being on campus. And as the pool of graduates, both at a bachelor’s and master’s level, increases, so too can more companies take advantage of deep learning. Even organizations without prior experience with such advanced infrastructure can put it to good use. If you’re one such enterprise interested in deep learning, this guide can help you along.

Build your vision

When implementing technologies as complex as deep learning, it’s important to clearly outline what objectives it’ll be used to achieve. In biotech, it can be used to identify molecules in target structures that provide therapeutic benefits. In manufacturing, it can facilitate predictive maintenance to cut costs. Clear and specific goals will help you apply deep learning more precisely in your operations. Starting out with smaller projects and working your way up over time can also help your organization slowly adopt deep learning with parameters that make it easier to measure success.

Collect comprehensive data sets

Afterward, gather the data necessary to help you meet your goals. Whether it be information on the target structures of therapeutic substances or on the inner workings of industrial equipment, this data needs to be as plentiful and diverse as possible. It’s also important to keep building these data sets even as deep learning is being implemented to keep predictions accurate and up-to-date. This will not only prevent these programs from developing biases from certain types of information but also make sure any deep learning projects can easily be scaled up or expanded across other areas of your business.

Equip yourself with a powerful infrastructure

Your organization will also need a place to store and process all the necessary data. Graphical processing units (GPUs), are crucial to any in-house infrastructure you’ll be using to do so. However, it can often be expensive to run things in-house if you’re just starting out. A more cost-effective alternative is the cloud. Companies like Amazon, Microsoft, and Google offer comprehensive cloud computing services so you don’t need to program your own infrastructure. Such services also allow you to access and process data remotely.

Leverage help from experts

Finally, have seasoned software engineers guide every step your organization takes. With software development programs now meeting demands for related talent, it’s best to maximize this opportunity to ensure your business implements deep learning in the most optimal fashion possible. These experts can go beyond simply advising which deep learning infrastructure will suit your operations the most. They can also help you establish roles and responsibilities across your organization, forming multidisciplinary teams that can implement systems for the constant monitoring and improvement of your deep learning model.

Initially, it may seem daunting to begin using tools like deep learning, especially if your organization has had no prior experience with the technology. However, following a few simple rules of thumb can ultimately help you fully reap the benefits it offers.

Exclusively  written for by Jessie Banks

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