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Complete Guide to Machine Learning for Cloud [2024]


Machine learning - this term seems to be everywhere these days. You’ve likely heard about machine learning being used in self-driving cars, medical diagnosis, and even targeting ads online. The key here is the cloud. The cloud provides unlimited storage and computing resources that can scale on demand to support ML training and inference. Companies can avoid investing in expensive on-premises GPU servers and only pay for what they use. 

The global Machine Learning market size is expected to grow from USD 26.03 billion in 2023 to USD 225.91 billion by 2030. But what does it mean for your business? In this complete guide, we’ll tell you - what it is, how it interacts with the cloud, types of cloud-based ML and ultimately why it matters. Let’s get started.

What is Machine Learning in the Cloud?

Machine Learning (ML) is a subset of artificial intelligence mimicking human learning. It allows machines to perform tasks through predictive capabilities based on historical data autonomously. Accurate ML model training demands substantial data, computing power, and infrastructure which is often challenging for organisations due to time and budget constraints. Cloud ML platforms provide requisite computing, storage, and services. It makes deploying ML more accessible, flexible, and cost-efficient. 

Similar Read: Exploring Risk Assessment with Machine Learning in Finance 

Benefits of ML in the Cloud

As promising as Machine Learning is, putting it to work has historically required tremendous effort. Building accurate models demands specialised data science skills. Deploying those models at scale calls for pricey hardware and complex infrastructure. This burdensome barrier to entry has kept advanced AI out of reach for most companies.

Fortunately, the cloud is removing those barriers with on-demand machine learning services. With cloud-based ML, companies no longer need to assemble expert AI teams or invest heavily in IT infrastructure to benefit from machine learning. Instead, they can simply tap into ready-made ML solutions tailored to their needs. You now have access to the same machine learning that underpins Netflix movie picks, Facebook feed rankings, and Amazon Alexa’s voice intelligence. But unlike tech giants, most businesses don’t need to employ elite AI talent or invest billions in Research and Development. A cloud democratises access to these cutting-edge capabilities.

The cloud can delivers modern toolkits, vast datasets, and flexible computing power. This means you can skip straight to building applications powered by advanced prediction, personalisation, automation and more. And thanks to the affordability of the cloud, you can start small and scale fast as needs grow. For example, Hyperstack’s cloud GPU pricing allows you to accurately track and forecast costs with our transparent pricing model, billed per minute.

Types of Cloud-Based Machine Learning Services

Let’s understand the types of cloud-based machine learning services offered currently: 

Artificial Intelligence-as-a-Service (AIaaS)

Artificial Intelligence as a Service (AIaaS) refers to cloud-based services that provide pre-built AI tools and applications to users on demand including natural language processing (NLP), computer vision, speech recognition, and machine learning.

With AIaaS, organisations do not have to invest in developing AI models and infrastructure from scratch. Instead, users can leverage pre-trained models, machine learning toolkits, and computing power hosted on a cloud services platform.

AIaaS allows even small and medium businesses to benefit from sophisticated machine learning capabilities without needing AI expertise or experience. For example, through AIaaS platforms, companies can access predictive analytics for tasks like forecasting demand, predictive maintenance on machinery, analysing customer churn, and more. The AI capabilities handle ingesting data, model building and testing in the cloud with no effort from the end user.

The AIaaS market size is valued at USD 9.3 Billion in 2023 but according to Reports and Data, global spending on AIaaS is forecast to grow at an annual rate of 42.6% to reach USD 55 billion by 2028. Two key factors driving adoption are affordable pricing models which eliminate upfront infrastructure costs as well as seamless integration of AI services with popular cloud data platforms.

By handling the complex machine learning pipeline behind the scenes AIaaS gives companies easy access to artificial intelligence capabilities which even small data science teams or developers can utilise through API calls. This opens exciting possibilities for organisations to build innovative products and improve services powered by robust and accurate predictive insights from AI.

GPU as a Service (GPUaaS)

GPU as a Service (GPUaaS) provides on-demand access to GPU computing power hosted in the cloud to train and deploy machine learning models. Instead of investing in high-end GPU servers on-premises, companies can leverage the latest GPU hardware virtually through public cloud services.

Training complex deep learning algorithms like image classifiers, speech recognition models and natural language processing requires very high parallel computing performance. GPUs with thousands of compute cores are remarkably well-suited for this over CPUs. Cloud-based GPUaaS enables data scientists to spin up hundreds of interconnected GPU instances to train neural networks rapidly.

According to Future Market Insights, the global GPUaaS market size is expected to be valued at USD 3,911.4 million in 2023. The overall demand for GPU as a Service is projected to grow at an annual growth rate of 40.8% between 2023 and 2033, totalling around USD 119 billion by 2032. Key growth factors include flexible consumption models and fast, affordable access to advanced hardware.

By providing convenient access to hardware acceleration, management tools, and ML frameworks, GPUaaS platforms enable data scientists to quickly train models with minimal infrastructure overheads. Companies can then easily host trained models to deliver low-latency inferences via prediction APIs without operating costly GPU servers continuously. For machine learning initiatives to be viable for many companies, leveraging a cloud-based GPU powerhouse is invaluable.

Cloud GPU for Machine Learning 

When it comes to accelerating machine learning workflows, leveraging cloud GPU virtual machines offers a plethora of benefits. Hyperstack provides access to a diverse range of cloud GPU for Machine Learning like the NVIDIA A100, H100 PCIe and H100 SXM

Here’s how you can train, fine-tune, and serve models faster with our Cloud GPUs for ML:

  1. Accelerated Training and Inference: Hyperstack GPUs excel in parallel processing, significantly boosting the training speed for intricate machine learning models. This acceleration expedites both the development and deployment phases of ML projects.

  2. Deep Learning Performance: Our GPUs are finely tuned for deep neural network training, empowering researchers and practitioners to tackle complex problems in deep learning with ease and efficiency.

  3. Containerised Deployment: Streamline complex ML workflows with pre-trained models and containerisation. This approach simplifies experimentation and deployment processes, facilitating smoother transitions from development to production environments.

  4. Transfer Learning: Leverage transfer learning techniques to fine-tune pre-trained models, saving valuable time and computational resources during the training phase. This approach is particularly advantageous for tasks requiring adaptation to specific domains or datasets.

  5. Natural Language Processing (NLP): Drive advancements in natural language processing (NLP) models, such as transformers, with the computational power of Hyperstack GPUs. Enable transformative applications like translation, text generation, sentiment analysis, and more.

  6. Computer Vision: Leverage our GPUs for vision tasks and classify, detect objects, and segment. This rapid parallel processing easily handles image and video analysis's hefty computational demands.

How to Use Cloud GPU for Machine Learning Projects?

To use a cloud GPU for Machine learning on Hyperstack, you need to sign up or login to the platform and then:

Create Your First Environment

The first step is to create an environment. Every resource such as keypairs, virtual machines, and volumes lives in an environment. 

Simply provide a name for your environment and choose the desired region where you want it to be located.

Import Your First Keypair

As the next step, import a public key that will grant you SSH access to your virtual machine. Ensure you have generated an SSH key pair on your local system beforehand.

To import the keypair, designate the environment where you wish to store it, assign a recognizable name for future reference, and input the public key of your SSH key pair.

Create Your Virtual Machine

With the environment and keypair set-up, proceed to create your virtual machine.

Choose the designated environment for VM creation, select a suitable flavour (specifications) for your VM, opt for the preferred OS image, assign a memorable name to your VM, select the SSH key for accessing it, and then initiate deployment by clicking the "Deploy" button. Your virtual machine is now ready for use.

To learn more please visit Hyperstack’s Documentation.

Limitations of Machine Learning in the Cloud

Using Machine Learning algorithms in the cloud comes with three key challenges that are:

  1. Data Privacy Concerns: Machine learning in the cloud raises significant data privacy concerns as sensitive data might be stored and processed on third-party servers in regions requiring separate regulatory compliance and data privacy laws. 

  2. Latency and Network Dependency: The reliance on network connectivity introduces latency issues, impacting real-time decision-making applications. Latency can degrade model performance and user experience, especially for applications requiring rapid responses.

  3. Scalability Challenges: While cloud platforms offer scalability, deploying machine learning models at scale can be challenging due to resource constraints and varying demands. Sudden spikes in workload or data volume can lead to performance degradation and increased costs.

Future Trends in Cloud-based Machine Learning

Future trends in cloud-based machine learning sound promising as technology continues to evolve, several key developments include 

  • Edge Computing: You can anticipate the integration of edge computing with cloud-based ML gaining prominence in your workflows. By leveraging edge devices' computational power and minimising latency, you can perform real-time inference and decision-making at the network's edge. This trend enables applications such as IoT, autonomous vehicles, and augmented reality to process data locally while still benefiting from the sophistication and scalability of cloud-based machine learning models.

  • Federated Learning: Federated learning, a decentralised approach to ML model training, is poised to become increasingly prevalent in cloud environments. This technique enables training models across distributed devices while keeping your data localised and private. Federated learning facilitates collaboration among multiple parties without compromising data privacy and security, making it suitable for sensitive data applications you may be handling.

  • AutoML Advancements: Cloud platforms will offer more sophisticated AutoML tools that streamline your entire ML pipeline, empowering you to build high-quality ML models efficiently, even if you're not an expert. This trend accelerates innovation across industries and makes ML more accessible to you.

  • Ethical AI and Responsible Cloud Practices: With the increasing societal impact of AI, there will be a growing emphasis on ethical AI and responsible cloud practices in user workflows. Tools and frameworks for bias detection, explainability, and fairness evaluation will become standard features in cloud-based ML platforms, ensuring that AI systems uphold ethical standards and mitigate potential biases and risks you may encounter.

Stay ahead of the curve with Hyperstack's next-generation NVIDIA Cloud GPUs. Leverage the latest advancements in AI hardware to build models that are scalable, efficient, and ready for the future. Don't settle for anything less, try Hyperstack today. Sign up here to get started.


What are the benefits of using machine learning in the cloud?

Using machine learning in the cloud offers scalability, cost-effectiveness, and flexibility. It enables seamless access to powerful resources, facilitates collaboration, and ensures security. 

Which cloud platform is best for machine learning?

Hyperstack cloud is one of the best platforms for machine learning tasks. We offer access to a diverse range of powerful NVIDIA GPUs, purpose-built for accelerating ML workloads. 

Which is the best Cloud GPU for Machine Learning?

The NVIDIA A100, H100 PCIe and H100 SXM are arguably the best cloud GPU for Machine Learning.

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