When organizations want to scale, one aspect of their business they need to fine tune is their workforce. Often, it’s a matter of either growing their workforce or augmenting it. How can optimizing a workforce help a business grow?
Let’s use shopping carts as an example. A neighborhood grocery store may be able to use very few employees to collect shopping carts, but a supermarket with much more traffic than the local store wouldn’t be able to keep up without optimizing their workforce. To solve this problem, supermarkets use cart collection machines that enable the same number of workers to move many more carts.
This simple example illustrates how a business must adapt as it attempts to scale profitability. The same is true of those that want to leverage data to grow their businesses. In this environment of data growth, machine learning (ML) is thriving like never before. We see it today in critical applications like fraud detection, workflow management, and shopping personalization. However, to use it effectively, business leaders must understand what powers ML.
The Different Learning Algorithms That Power ML
Machine learning solutions include all of the software, tools, and intellectual property you need to run your AI, whereas an ML tool may be just one component of the solution. This distinction is important to those who are trying to solve problems with machine learning and AI because a single tool will not likely provide all of the functionality they need.
Where should businesses start when building ML solutions?
Early on, you need to decide on how you will train your machine-learning tools. This choice depends heavily on your purpose, as each learning algorithm excels in different areas. The four most popular machine-learning methods are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
- Supervised learning. This type of learning is when the algorithm receives both a set of correct inputs and outputs to learn from. The algorithm can then learn by comparing its output with the correct data it has already received. Supervised learning algorithms are used to extrapolate conclusions from historical data.
- Unsupervised learning. This type of learning algorithm does not receive the correct answers, and its purpose is to find patterns within the data. Unsupervised learning is ideal for finding patterns in customer data, like when data is used to determine shopping habits.
- Semi-supervised learning. Like supervised learning, this is used to extrapolate conclusions based on historical data. However, to save on the costs of labeling data, semi-supervised learning algorithms use a mix of labeled and unlabeled data.
- Reinforcement learning. Through trial and error, algorithms learn which actions will give them the best results. Over time, the algorithm will attempt to maximize the expected reward. One use case for this type of AI would be in gaming. For example, AlphaGo Zero, which was trained with reinforced learning algorithms, was the first computer program to defeat a world champion in the game of Go.
It’s important for organizations to first identify their goals and the algorithm that matches up with their needs and budget, as this will clarify the hardware they’ll need to make it work. Machine learning is dependent on an abundance of varied, high-quality data. But, to ensure ML can scale with your business, it’s crucial to pair this up with the right hardware.
Machine Learning Hardware That’s as Powerful as You Need It to Be
Machine learning thrives on hardware that is scalable, flexible, and power-efficient. How can businesses balance the need for high-performance hardware, scalability, and budget? The key is ensuring that components are in harmony with each other and with your workload needs.
How does this affect your hardware choices?
When operating at scale, you must carefully consider the price of every component and the reward you’ll get for choosing a higher-priced, higher-performance part. Ensuring harmony means choosing components that allow the rest of the hardware to operate at peak performance. For example, if you choose memory that provides way more capacity than your processors can leverage, you will end up wasting money on parts that result in diminishing returns. In addition to making sure your hardware components are compatible, you also have to consider the computing requirements of AI and ML.
AI and machine learning need to process immense amounts of data in parallel, communicate regularly with other hardware on the network, and ensure sensitive data isn’t compromised. Fog computing hardware can help organizations process data closer to the source, giving them better performance and more control over who has access to the data. When business leaders factor in ML goals, hardware balance, and ML’s unique needs, they can build infrastructure that is aligned with their business goals.
Build Balanced Machine Learning Hardware with Intequus
Those working on machine learning problems are trying to pave new paths in their industry. This goal leaves little time to optimize hardware for their needs. Our team at Intequus helps automate this part of the equation. From design to deployment and support, we can help you build ML hardware that helps your company scale with ease and support that hardware throughout its lifecycle. Talk to one of our experts and channel your focus into solving your ML problems.