Machine Learning (ML) is a data analysis method that automates the construction of an analytical model. It is a branch of Artificial Intelligence (AI) based on the idea that systems can be trained from data, identify patterns, and make decisions with minimal human involvement.
With the latest computing technologies, Machine Learning now differs from that of the past. It is about pattern recognition and the theory that computers can learn without programming for specific tasks. The iterative aspect of ML is important because models exposed to new data can adapt autonomously. They learn from previous computations to provide reliable results.
ML allows companies to extract meaningful information from a huge amount of raw data. When implemented correctly, technology can serve as a solution to various business tasks, as well as predict future business development.
In this article, we explore cloud ML capabilities and their potential uses across businesses.
Challenges in implementing Machine Learning
Solutions based on artificial intelligence are entering our daily lives gradually, radically changing the usual operations and things. Machine Learning and AI are changing the financial sector, power industry, retail, healthcare, high technology, and telecommunications. They automate business processes; predict demand and sales, and free analysts from routine tasks, allowing them to focus on strategy. However, the integration of Machine Learning within businesses is a challenging task.
The biggest problems with implementing ML are as follows:
Lack of expertise in data science, which is not so easy to obtain.
The need for highly specialized professionals in programming and algorithmic tasks.
ML workloads require great processing power – specialized computing equipment adds to the cost of development, infrastructure, and workforce.
These are the main reasons why this technology was out of reach to most businesses. For quite a long time ML was affordable mainly for IT-giants, like Google, Amazon, Apple, e-Bay, and Facebook.
Amazon was one of the first to use AI to develop recommendations for its products. Netflix has undermined the global film industry by offering users high-quality media content through unique reference algorithms. The focus of research and development for Apple is to improve the virtual assistant Siri, as well as machine translation, object recognition in photos, and recommendatory algorithms of streaming services.
GPU Cloud for Machine Learning
Today, with public cloud development, Machine Learning becomes affordable for a variety of enterprises. It offers a significant level of flexibility to meet challenges in implementation and adapts to data science needs. Cloud resources significantly lower the financial barrier to building infrastructure for ML workloads.
One of the most important considerations related to Machine Learning is hardware. Training models is a very compute-intensive task, which requires major parallel computing resources. On traditional CPU-based processors, it could take days. Powerful graphics processing units (GPUs) significantly reduce processing time, as they are specifically designed for AI and Machine Learning workloads.
Cloud providers offer virtual servers with various GPUs for Machine Learning. NVIDIA GPUs are considered the world's most powerful accelerators for in-depth training.
There are four main ways in which cloud Machine Learning benefits business.
Cloud offers a pay-per-use model. This eliminates the need for companies to invest in expensive Machine Learning systems that they will not use every day. You can use the power of the GPU without investing in expensive hardware. Besides, public clouds provide cheap data storage.
No expertise required
The number of companies that have experience with AI or ML is still relatively small. This is mainly because IT teams are not qualified enough to implement and support AI and ML. However, in recent years we can observe an explosive growth in the popularity of such projects. And cloud technologies have contributed to this in many ways.
Using the cloud means that companies do not have to worry about having a team of data scientists. With a cloud platform, they can implement AI without the need for in-depth knowledge.
Easy to scale up
If a company is just experimenting with Machine Learning and wants to explore its capabilities, it makes no sense to run it in full at once. With cloud ML, enterprises can first test and implement small projects in the cloud and then scale them up as needs and demand grow. The pay-per-use model simplifies access to more complex capabilities without having to buy and deploy new hardware.
Machine-learning use cases
Most industries working with large volumes of data have admitted the value of Machine Learning technology. By discovering insights in data (often in real-time) organizations can work more efficiently or obtain advantages over their competitors.
Banks and other financial industry enterprises can use cloud-based Machine Learning for two key purposes: to identify important data, and prevent fraud. They can identify investment opportunities or help investors understand exactly when to trade. Intelligent data analysis can also identify customers with high-risk profiles or use cyber surveillance to accurately determine fraud.
Public security agencies and utilities especially need Machine Learning because they have a variety of data sources from which they can obtain information for analysis.
Machine Learning is evolving in the healthcare industry, with the emergence of portable devices and sensors that can use data to assess patient health in real-time. Medical experts analyze data to identify trends that lead to better diagnosis and treatment.
Websites recommending products that you may like based on previous purchases use Machine Learning to analyze your shopping history. Retailers rely on Machine Learning to collect, analyze, and use data to personalize their purchases, conduct marketing campaigns, optimize prices, plan deliveries, and understand customer needs.
Machine learning workloads require high processing capabilities. Compared to CPU, graphic processing units provide increased processing power, memory bandwidth, and parallelism capabilities. Renting a virtual server with GPU helps to meet the specific requirements of some ML workloads, with the benefits of a cloud environment. For organizations that just getting started, cloud infrastructure makes more sense. Such deployments allow you to start with minimal upfront investment.