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Cloud Deep Learning: 3 Focus Areas & Key Things to Know in '24

Updated on Mar 14
6 min read
Written by
Burak Ceylan
Burak Ceylan
Burak Ceylan
Burak is an Industry Analyst in AIMultiple focused on cloud AI and accounts payable.

He received his Masters' degree in Political Science from Middle East Technical University and currently a phD student at the same university. He has background in researching location-based platforms.
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Cloud deep learning is the integration of cloud computing and deep learning models that can process inputs through different layers. By developing deep learning, businesses can perform more complex tasks compared to classical algorithms. There are various facets to achieve this goal.

Choosing classical models can require excessive computational (hardware) energy and large volumes of data. Cooperation with cloud technology can by-pass these bottlenecks. However, cloud technology is a vast field and before making a decision, businesses need to take into account different factors.

Here, we will explain cloud deep learning by delving into its practices, exploring its various facets, the significance of data, and the roles it plays in key technologies like Pytorch and GPUs.

Cloud Deep Learning: what to consider?

While exploring cloud deep learning, several factors come into play that can significantly impact the decision-making process. Here’s a detailed look at each dynamic:

GDPR compliance

GDPR (General Data Protection Regulation) compliance means ensuring EU based data privacy and security standards are met. This includes data encryption, regular audits, and transparent data processing protocols. An owned model may offer more direct control over GDPR compliance, but managing it in-house requires significant resources. Cloud providers often have robust systems in place for GDPR compliance that can ease the burden on individual organizations.

Source: Emotiv.1 

Data privacy

Employing encryption and anonymization techniques; regularly updating privacy policies are some of the ways to achieve data privacy. Cloud solutions often have advanced security measures in place. However, an owned model allows for tighter control over data access. The choice depends on the organization’s capability to implement stringent data security measures.


Costs are a major consideration, encompassing both the initial setup and ongoing operational expenses. Evaluate the total cost of ownership (TCO) for both cloud and owned models. Consider not just the initial investment but also long-term operational costs.

Cloud solutions typically operate on an OPEX model (operational expenditure) with pay-as-you-go pricing, reducing upfront costs. Owned models involve higher CAPEX (capital expenditure) but can be more cost-effective in the long run, especially for large-scale operations.

Ease of use 

Cloud solutions often stand out in terms of ease of use, with managed services, pre-built models, and scalable infrastructure. Owned models offer customization but require more technical expertise to set up and maintain.

Overall, deciding between cloud deep learning and an owned model, the trade-off involves balancing OPEX and CAPEX, as well as weighing the benefits of speed and ease of use against costs. Cloud solutions offer scalability, ease of use, and lower initial costs, but they may involve higher long-term operational costs and less control over data. On the other hand,, owned models require higher upfront investment and expertise but provide more control and potentially lower long-term costs.

Facets of cloud deep learning performance

Data: Data is the lifeblood of deep learning models. In a cloud setting, massive datasets can be stored, processed, and analyzed. Cloud providers offer services to handle large volumes of data, essential for training complex machine learning models.

Diminishing Software and Hardware Dimensions

  1. Cloud GPUs: These specialized processors in the cloud significantly enhance the efficiency of model training.
  2. Deep Learning containers and frameworks: Technologies like Google Compute Engine and cloud-based deep learning containers allow for streamlined deployment and management of deep learning applications.

The Role of GPUs in cloud deep learning Their ability to process multiple computations simultaneously makes them ideal for the heavy computational demands of deep learning. Cloud providers offer cloud GPUs as part of their services, allowing users to leverage their power without the need for physical hardware.

If interested, check out our data-driven list of cloud GPU platforms.

Software, hardware and data configurations in cloud deep learning

HPC (High performance computing) systems

HPCs are designed to handle and process large-scale data which is a core requirement in training and deploying deep learning models. Their ability to perform parallel processing makes HPCs ideal for the intensive computational demands of deep learning tasks. HPCs are commonly used in research and industrial settings for complex simulations, data analysis, and training large-scale deep learning models. HPCs often combine multiple GPUs and CPUs in a single framework.


CPUs typically offer fewer cores designed for sequential serial processing. A GPU, on the contrary, can boast hundreds or thousands of smaller cores designed for multi-threading and managing parallel processing tasks. A cloud GPU, on the other hand, is a model of utilizing GPUs as a service via cloud computing platforms. Similar to standard cloud services, it offers the ability to tap into high-performance computing resources on a spot or on-demand basis. Their ability to process multiple computations makes them specifically train large neural networks and algorithms.

For more information: 


FPGAs (Field-programmable gate arrays) are highly customizable. This allows businesses to optimize specific computational tasks in deep learning for efficiently handling complex algorithms. FPGAs can also be reprogrammed to suit different needs. They offer a balance between the general-purpose nature of CPUs and the specialized nature of GPUs. FPGAs are particularly useful in scenarios where deep learning models need to be frequently updated or customized, such as in edge computing where low latency and adaptability are key.

TPUs (TensorFlow processing units)

TPUs are designed specifically for TensorFlow, an open-source platform for machine learning. They are specialized application-specific integrated circuits (ASICs) built to enhance the speed and efficiency of tensor operations, which are fundamental in the computations of neural networks. 

Process: TPUs primarily handle matrix processing using a systolic array architecture that can perform multiply and accumulate operations. Data flows into the TPU via an infeed queue, is temporarily stored in High Bandwidth Memory (HBM), and then processed. The results are then queued in an outfeed for retrieval by the host. The TPUs perform matrix operations by loading data and parameters from the HBM into the Matrix Multiplication Unit (MXU), where calculations occur without the need for additional memory access. This design enables TPUs to execute complex neural network calculations.2  

Figure 2. Frequency of TPU usage.

Software dimension

Common frameworks

Frameworks such as PyTorch offer libraries and tools that facilitate the building, training, and testing of deep learning models. Deep learning systems, a collection of models interacting with each other, can be encapsulated by PyTorch. This means that it is built for more complicated research and production cases, where many models interact with each other in complex manner. The framework can be used to make codes more readable by decoupling the research code. Thus, on the software side, compatibility with frameworks such as Pytorch is crucial for decision making in cloud deep learning.

For those interested, here is our article on PyTorch Lightening, a more recent version of PyTorch.

Some providers also offer deep learning drivers that are already installed in cloud GPUs.3  These pre-configured models are often optimized for performance, ensuring that users can leverage hardware accelerations without manual adjustments. These platforms allow managing GPUs through dashboards, creating GPUs with APIs and integration with different libraries.

No-code (drag & drop) models are used in prototyping and experimentation. They allow users to visualize and manipulate models easily. However, they may offer less customization than code-based environments.

No-code (drag & drop) model

No-code machine learning platforms enable users to create ML models and make predictions using a visual, drag-and-drop interface. They are commonly used to handle data collection, cleaning, model choice, training, and deployment automatically. 

In traditional ML, skilled data scientists program models, typically in Python, involving hands-on data preparation and model tuning. No-code platforms simplify this process. They allow users without technical expertise to build models independently. 

Some platforms can bypass the necessity of choice by including both no-code development, hardware and data functionalities.4   

Data dimension

Data labeling service

Data labeling is essential for training deep learning models. It provides the necessary information that models learn from. Accurate labeling directly impacts the effectiveness of the models. Cloud-based data labeling services offer solutions for handling large datasets, often involving crowdsourcing and automated tools. They enable processing and management of data, crucial for deep learning projects that require vast and well-annotated datasets.

Data preparation: services support ETL & ELT

ETL (extract, transform, load) & ELT (extract, load, transform) are key processes in data preparation. ETL involves extracting data from various sources, transforming it into a suitable format, and then loading it into a system for analysis. ELT loads data first and then transforms it within a target system. 

In the context of cloud deep learning, investigating the data services offered by your cloud provider to determine if they support ETL, ELT or both is important. It’s also important to identify which data storage, database solutions you will utilize.

Scale up & out

Scaling up mostly refers to increasing the capacity of existing hardware or software to process data; upgrading a server with more memory or a more powerful CPU. In the context of deep learning, this might involve using more powerful machines to handle complex computations. 

Scaling out involves adding more nodes or instances, like additional servers, to a system. This is particularly useful in cloud environments for parallel processing of data or distributing deep learning tasks across multiple machines. 

Scalability is a major advantage of cloud computing in deep learning. Cloud services allow visualized data processing, often supporting large-scale. Through using these platforms, businesses can evaluate whether they need more/less hardware configurations such as GPUs.

For more information: 

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Burak Ceylan
Burak is an Industry Analyst in AIMultiple focused on cloud AI and accounts payable. He received his Masters' degree in Political Science from Middle East Technical University and currently a phD student at the same university. He has background in researching location-based platforms.

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