The Benefits and Challenges of Edge Machine Learning

October 29, 2021

Explore the rise of edge machine learning in IoT projects, its advantages like cost savings, security, and enhanced user experience, and challenges like limited computational power. Discover how Wallaroo can simplify edge ML deployments for optimal results.

As the number of enterprise IoT projects explodes, more than half are focused on cutting costs. According to a new analysis of IoT use cases, these projects mostly cover areas like remote asset monitoring and control, process automation, and fleet management.

Projects like predictive maintenance rank lower, even though they are vital for manufacturers who want to prevent highly expensive breakdowns and production downtime. One reason for this is organizations need the benefits of machine learning quickly, but cloud computing for machine learning comes with uncomfortably large bills. This is where edge machine learning becomes an obvious choice for several reasons.

Machine learning running at the edge

Edge ML runs machine learning processes on the individual device or an environment closer to the device than the cloud. Some devices can apply the necessary learning models themselves for real-time inference. Others need a local network or server to run them on. Either way, the data is stored and processed on or close to the endpoint that collects it.

For instance, this means that a modern car can run machine learning processes on its own hardware. Without accessing the cloud, it can react to instructions and actions from the driver and store the learnings. This is particularly useful when the car has no connection to the internet or needs to react quickly.

The benefits of edge machine learning

In short, machine learning makes IoT and other connected devices safer, cheaper, and a lot faster. It also lets customized models run where they’re needed, which is useful in enterprises with a large number of devices. Here are some of the main benefits:

Lower bandwidth needs: Novel cars and planes accumulate an astounding amount of data every day. Cars can generate 1TB in a single day, while planes can collect several terabytes. As older cars and planes retire to make way for new ones, this would make for some serious bottlenecks on the internet.

Edge ML saves massive amounts of bandwidth. Cars, planes, and other machines run machine learning on the collected data by themselves and only send off what they need more power to process – or what feedback their manufacturer needs to improve all endpoints.

Lower costs: Cloud computing can be expensive. With the enterprise’s major focus on cutting costs, edge machine learning is an obvious choice. When machine learning is done on the individual device or machine, the expenses for cloud computing and bandwidth are reduced considerably.

Easier access to ML: As bandwidth needs and costs are lowered, the benefits of machine learning are made available to a larger group of the population of our planet.

Higher security: When the device can offer instant feedback without being connected to the internet, autonomous cars and manufacturing robots can recognize and avoid dangerous situations as they happen. At high speeds of driving or production, the situation could have escalated beyond control before the inference made it back from the cloud.

Increased privacy: When less data is shared, fewer data packages can be compromised in transit. With a significant amount of the data being processed locally, you have better control over your level of privacy. Edge machine learning can process video and audio data in or close to real-time. Therefore the source data can be deleted as soon as the process is complete. This further increases privacy and decreases the need for storage and bandwidth.

Better user experience: Nobody wants to wait around for a joke or witty comeback from their voice assistant. Likewise, most of us would like our cars and planes to be able to function optimally even when they are out of range of a proper connection to the internet. Aside from our safety the user experience is also significantly improved with the immediate feedback in harmless situations.

The challenges of edge machine learning

Even though it’s very valuable to have devices, machinery, and local networks run on edge ML, there is also a range of challenges to overcome.

Limited computational power and memory: Endpoints and their local edge environments have limited resources to run machine learning processes. In contrast, some of the most widely used technologies for deploying and running models are severe resource hogs. As a result, they still run better in the cloud than on the edge.
Limited bandwidth and connectivity issues: Most existing machine learning inferencing solutions still assume a good and stable connection to the internet. So even though edge machine learning makes this less important in theory, reality just hasn’t completely caught up yet.
Hybrids are common and difficult: In most cases, there will be a need to run a hybrid machine learning process. Because computation power at the edge is limited, some data often needs to be sent to the cloud for model development, validation, and experimentation. Some data needs to go to the cloud for additional computation and then be distributed to the right edge locations for deployment. Even though this hybrid scenario is very common, it is also very complex to manage.
Deploying models at specific locations is problematic: Another issue is the ability to easily and efficiently deploy many models to many edge devices. Oftentimes, locations need models to be very customized to fit the use case (i.e. locations, specific buildings for specialized purposes, etc.). This type of specialization can lead to deployment management issues which can have a ripple effect on the visibility and results needed.

Make your Edge ML a breeze with Wallaroo

As an enterprise platform for production AI, Wallaroo was built to knock out the biggest barriers between you and the efficient processing of your data. Wallaroo’s unique approach to production AI gives any organization faster time-to-market, audited visibility, scalability, and measurable business value.

Our AI-driven initiatives are designed to allow data scientists to focus on value creation instead of sweating the small stuff. Here are just a few of the benefits you can expect with Wallaroo:

Run your models in the cloud, on-premises, or at the edge
High velocity, low latency data processing on small-footprint hardware
Efficient ML pipeline processing engine, resulting in up to 80% reduction in computing costs
Fast and simple one-click deployment

For one F100 enterprise client, the Wallaroo platform increased data processing by 553%, reduced computing costs by 75%, and enabled the team to operationalize 50% more models.

Read the full story on how this established healthcare insurance corporation leveraged the power of Wallaroo for their security machine learning.

In our upcoming post about Wallaroo and Edge deployments, we will cover how you can use our platform to run powerful processes with limited resources.
Get in touch today to start running edge machine learning with Wallaroo.