Machine learning (ML) has shifted from being a niche research field to a powerhouse behind many technologies we use daily. From personalized recommendations on streaming platforms to chatbots and image recognition, ML’s influence is everywhere. But what exactly is machine learning, and why should you invest time in learning about it? This blog will walk you through ML’s fundamentals, explain what you need to know, and outline a practical step-by-step plan to start your ML journey.

As the demand for large language models (LLMs) and AI-powered applications continues to grow, businesses are facing challenges in scaling compute capabilities and managing costs. GPUs have become the cornerstone of AI innovation, but their integration requires scalable and efficient solutions tailored to enterprise needs.

As businesses evolve in today’s digital landscape, the need for efficient and scalable computing resources has become paramount. In the early days of the Internet, large corporations would build or rent out large data centers to run their applications and serve customers. This was great as they could use dedicated hardware and expand as they pleased.

Technology is advancing rapidly, and with it comes a growing demand for powerful computers, especially in fields such as machine learning (ML), artificial intelligence (AI), and high-performance computing. As these areas develop, the size and complexity of the data they handle also increases. This surge in computing power requirements necessitates new methods for processing large amounts of data efficiently, without sacrificing accuracy or speed.

As someone deeply embedded in the cloud-native space, I’ve witnessed how the rapid evolution of cloud computing has unlocked remarkable opportunities—but also introduced significant challenges. Recently, I revisited some research we conducted back in 2022, which revealed a critical trend: despite the hundreds of services offered by the major cloud providers (the Big Three hyperscalers), most businesses use fewer than 10 services from their cloud provider.

To read the full findings from this research, visit the AI for All white paper by clicking here. Artificial intelligence (AI) and machine learning (ML) are reshaping industries and unlocking unprecedented opportunities. However, the widespread adoption of these technologies hinges on addressing a critical issue: the accessibility of GPUs.

Last year, we reflected on the progress that AI made in 2023 with tools like ChatGPT lowering barriers to entry and driving broader adoption across industries. Looking ahead, we anticipated even greater advancements in AI's accessibility and governance, including the democratization of technology, enhanced large language models for specific applications, and growing concerns around the ethical implications of synthetic content and intellectual property rights.