How Can You Do Deep Learning in the Cloud?
Deep learning is at the center of most artificial intelligence initiatives. It is based on the concept of a deep neural network, which passes inputs through multiple layers of connections. Neural networks can perform many complex cognitive tasks, improving performance dramatically compared to classical machine learning algorithms. However, they often require huge data volumes to train, and can be very computationally intensive.
Cloud computing services are helping make deep learning more accessible, making it easier to manage large datasets and train algorithms on distributed hardware.
Cloud services are an enabler for deep learning in four respects:
- Provide access to large-scale computing capacity on demand, making it possible to distribute model training across multiple machines.
- Provide access to special hardware configurations, including GPUs, FPGAs, and massively parallel high performance computing (HPC) systems.
- Do not require an upfront investment—you can get advanced hardware, or large quantities of hardware, without having to purchase it. Pay only for the time you use.
- Assist with management of deep learning workflows—cloud services provide advanced features for managing datasets and algorithms, training models and deploying them efficiently to production.
This is part of an extensive series of guides about IaaS.
In this article, you will learn:
- Top Deep Learning Services in the Cloud
- ~IaaS vs. PaaS
- ~Deep Learning on AWS with SageMaker
- ~Google Cloud Machine Learning Services
- ~Microsoft Azure Machine Learning
- How to Choose a Cloud Deep Learning Platform
- ~Data Preparation
- ~Scale-Up and Scale-Out Training
- ~Deep Learning Frameworks Support
- ~Pre-Tuned AI Services
- ~Monitor Prediction Performance
What Are the Most Popular Deep Learning Services in the Cloud?
Let’s briefly review the deep learning offerings of major cloud providers—Amazon, Google Cloud, and Microsoft Azure.
IaaS vs. PaaS
In each of these clouds, it is possible to run deep learning workloads in a “do it yourself” model. This involves selecting machine images that come pre-installed with deep learning infrastructure, and running them in an infrastructure as a service (IaaS) model, for example as Amazon EC2 instances or Google Compute Engine VMs.
All the cloud providers we review below offer compute instances suitable for deep learning models, which provide specialized hardware such as graphical processing units (GPU), field-programmable gate arrays (FPGA) and TensorFlow Processing Units (TPU). To learn about the compute options offered by each cloud provider, refer to our articles about:
- Google TPU
- AWS GPU
- Azure GPU
Below, we focus on the platform as a service (PaaS) offering each cloud provides for deep learning users. These PaaS offerings provide the hardware needed for deep learning workloads, as well as software services for managing deep learning pipelines, from data ingestion to production deployment and real-world inference.
Deep Learning on AWS with SageMaker
Amazon Web Services provides the SageMaker service, which lets you build and manage machine learning models on the cloud, with a focus on deep learning.
- SageMaker services include:
- Ground Truth—lets you create and manage training data sets
- Studio—cloud-based development environment for machine learning models
- Autopilot—builds and trains models automatically
- Tuning—helps tune hyperparameters for a model
- Supports Jupyter notebooks—allowing users to share and collaborate on their own models and code.
- AWS Marketplace—provides pre-built algorithms and models created by third parties, which can be purchased on a pay-per-use basis.
- Framework support—supports all popular deep learning frameworks including TensorFlow, PyTorch, MXNet, Keras, Gluon, Scikit-learn, Horovod, and Deep Graph Library.
Learn more in our guide to AWS deep learning
Google Cloud Machine Learning Services
Google's set of machine learning services, together called Cloud AI, includes general purpose and dedicated services for specific use cases:
- Cloud AutoML suite—lets you build, train, and deploy models to production using cloud infrastructure
- AI Hub—provides a repository of components and algorithms that can be used to build models. Unlike the AWS model, AI Hub is focused on free knowledge sharing, not on commercial offerings of AI components.
- Data labeling service—lets you prepare and identify data for machine learning models.
- Visual AI and Video AI—these are two purpose-built services that provide preconfigured deep learning pipelines for processing image and video data.
Microsoft Azure Machine Learning
Azure Machine Learning is a complete environment for training, deploying, and managing machine learning models.
Key features of Azure Machine Learning:
- Drag-and-drop model designer—used to build machine learning models with no code. The designer supports several neural network architectures, including two-class classification, multi-class classification, neural network regression, DenseNet and ResNet.
- MLOps—supports a DevOps-style method for building and managing machine learning pipelines and workflows.
- Security and governance—integrated into the service, letting you verify compliance of machine learning processes, and perform identity and privacy management according to your organization’s governance policies.
- Frameworks support—supports PyTorch, TensorFlow, Keras, MXNet, scikit-learn, and Chainer.
Learn more in our guide to Azure deep learning
How Should You Choose a Cloud Deep Learning Platform?
Here are a few key considerations when selecting your cloud-based deep learning service.
Data preparation can be one of the heaviest and most sensitive parts of a deep learning project. There are two common ways to prepare large volumes of data for analytics, which are also used to create deep learning datasets from raw data:
- Export, transform, load (ETL)—transforms data as it is pulled from the source and creates a ready-made dataset that can be used for analytics purposes.
- Export, load, transform (ELT)—provides greater flexibility, lets you store raw data in a data lake and then transform it into the required format on demand.
Check which data services are provided by your cloud vendor and whether they support ETL, ELT, or both. Understand which data storage, database or data warehouse services you will use, and how they can make data preparation easier.
Scale-Up and Scale-Out Training
Data scientists typically start by developing a model on a local notebook, but it is not feasible to train most deep learning models on a local workstation. A key capability of a cloud deep learning service is the ability to integrate with notebooks and push training jobs seamlessly to cloud-based compute instances.
Evaluate the process and how easy it is to run training jobs on hardware like GPUs, TPUs, and FPGAs, manage these jobs across data science teams, visualize and interpret their results.
Deep Learning Frameworks Support
Each cloud machine learning service supports different frameworks. You can typically get the broadest framework support in an IaaS model, when deploying deep learning directly on compute instances. However, if you use a full ML Ops platform, you will be limited to the frameworks it supports.
Look for support of the following frameworks, which your data science team may need to use now or in the future:
- Deep learning frameworks—TensorFlow, PyTorch, Keras, MXNet, Deep Java Library
- Classical machine learning—Scikit-learn, R, Spark MLlib, H2O.ai, Java-ML
- Job scheduling and distribution—Horovod, Kubernetes, Slurm, LSF (see our detailed comparison of job schedulers)
Also evaluate the ability to integrate your own code and algorithms with the platform’s library of built-in algorithms. This can improve productivity, because you can draw on existing building blocks and only develop unique aspects of your model.
Pre-Tuned AI Services
Most cloud platforms provide pre-trained, pre-optimized AI services for many applications including:
- Image classification
- Object recognition
- Video data extraction
- Language translation
- Speech synthesis
- Recommendation engines
The advantage of these types of services is that they have been trained on massive data volumes that are not available to individual companies. They can provide very high accuracy for general use cases, and provide excellent performance and low latency in production. Best of all, they are ready to use out of the box.
Monitor Prediction Performance
Deploying a model is only the start, not the end point, of your AI journey. Data changes and user requirements change, and it is essential to monitor a model’s performance over time, tune it, augment it, and if necessary, replace it. Evaluate the tools a cloud service provides for monitoring model performance when it is already in production, and how easy it is to release updates and improvements to live deep learning models.
Deep Learning in the Cloud with Run:AI
Run:AI automates resource management and orchestration for machine learning infrastructure. With Run:AI, you can automatically run as many compute intensive experiments as needed.
Our AI Orchestration Platform for GPU-based computers running AI/ML workloads provides:
- Advanced queueing and fair scheduling to allow users to easily and automatically share clusters of GPUs,
- Distributed training on multiple GPU nodes to accelerate model training times,
- Fractional GPUs to seamlessly run multiple workloads on a single GPU of any type,
- Visibility into workloads and resource utilization to improve user productivity.
Run:AI simplifies machine learning infrastructure orchestration, helping data scientists accelerate their productivity and the quality of their models.
Learn more about the Run:AI GPU virtualization platform.
Learn More About Cloud Deep Learning
There’s a lot more to learn about cloud deep learning. To continue your research, take a look at the rest of our blogs on this topic:
AWS Deep Learning: Choosing the Best Option for You
Amazon Web Services (AWS) is a cloud computing pioneer providing a wide range of scalable, affordable, and innovative cloud services, including a dedicated solution for deep learning. AWS offers a fully-managed machine learning service called SageMaker, and AWS Deep Learning AMI (DLAMI), which is a custom EC2 machine image, as well as deep learning containers.
This article explains in-detail the various deep learning services offered by AWS, and how to leverage AWS technology for training deep learning models.
Read more: AWS Deep Learning: Choosing the Best Option for You
Azure Machine Learning: From Basic ML to Distributed Deep Learning Models
Microsoft Azure is a top cloud computing vendor offering many enterprise-grade services, including a dedicated solution for machine learning and deep learning, called Azure Machine Learning (Azure ML). Azure ML leverages virtual machines (VMs), datasets, datastores, code models, and deployment environments to enable effective training of deep learning models.
This article explains how Azure ML works, and how to perform distributed training of deep learning models on Azure.
Read more: Azure Machine Learning: From Basic ML to Distributed Deep Learning Models
Google TPU: Architecture and Performance Best Practices
Google provides cloud computing services, including dedicated solutions for artificial intelligence (AI), machine learning, and deep learning. Google has long been considered a pioneer and innovator in AI and software development, creating solutions that are adopted worldwide. Tensor Processing Units (TPUs) are another Google innovation, created to help accelerate machine learning.
This article explains what a TPU is, how the technology works, and explores key best practices for optimal cloud TPU performance.
Read more: Google TPU: Architecture and Performance Best Practices
Google Cloud GPU: The Basics and a Quick Tutorial
Google Cloud Platform (GCP) is the world’s third largest cloud provider. Google offers a number of virtual machines (VMs) that provide graphical processing units (GPUs), including the NVIDIA Tesla K80, P4, T4, P100, and V100.
Learn about Google Cloud GPU and TPU options, and learn how to set up a compute instance with an attached GPU in a few easy steps.
Read more: Google Cloud GPU: The Basics and a Quick Tutorial
Triton Inference Server: The Basics and a Quick Tutorial
NVIDIA’s open-source Triton Inference Server offers backend support for most machine learning (ML) frameworks, as well as custom C++ and python backend. This reduces the need for multiple inference servers for different frameworks and allows you to simplify your machine learning infrastructure
Learn about the NVIDIA Triton Inference Server, its key features, models and model repositories, client libraries, and get started with a quick tutorial.
Read more: Triton Inference Server: The Basics and a Quick Tutorial
See Our Additional Guides on Key IaaS Topics
Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of IaaS.
Authored by NetApp
Learn about cloud migration and what major challenges to expect when implementing a cloud migration strategy in your organization.
See top articles in our cloud migration strategy guide:
- Cloud Migration Tools: Transferring Your Data with Ease
- Cloud Data Integration 101: Benefits, Challenges, and Tools
- 3 Cloud Migration Approaches and Their Pros and Cons
Authored by Lumigo
Learn about the AWS ecosystem on its services, understand the core Lambda functionalities, and discover AWS Lambda monitoring functionalities.
See top articles in our guide to the AWS serverless ecosystem:
- The AWS Serverless Application Model
- AWS Step Functions - Limits, Use Cases, Best Practices
- What is AWS X Ray?
Authored by Spot.io
Learn about financial and economic aspects of cloud computing, how to optimize your cloud costs, and strategies for getting a better return on your cloud investments.
Google Cloud Vertex AI allows you to build, deploy, and scale machine learning models faster, with pre-trained models and custom tooling within a unified artificial intelligence platform.What are the two different deep learning platforms? ›
Neural Designer, H2O.ai, DeepLearningKit, Microsoft Cognitive Toolkit, Keras, ConvNetJS, Torch, Gensim, Deeplearning4j, Apache SINGA, Caffe, Theano, ND4J, MXNet are some of the Top Deep Learning Software.Which cloud technology is best for machine learning? ›
Amazon Web Services (AWS) is one of the most popular cloud computing platforms for Machine Learning, developed by Amazon in 2006. There are so many products provided by AWS as follows: Amazon SageMaker: This product primarily helps to create and train machine learning models.Which is the No 1 online learning platform? ›
1. Coursera. Coursera is a popular online education platform that offers courses from top education providers around the world. Over the years, the company has grown rapidly and provides the world's best standards of education through MOOC.What is the biggest learning platform? ›
- LinkedIn Learning.
- Hostwinds. Hostwinds is a cloud solution that gives you a good run for your money. ...
- Cloudways. Trusted by 75,000+ businesses, Cloudways makes the list for a reason. ...
- Hostinger. ...
- AWS (Amazon Web Services) Cloud. ...
- Google Cloud. ...
- Microsoft Azure. ...
- IBM Cloud. ...
- Oracle Cloud.
Due to its almost 7-year operating history, AWS has greater resources, infrastructure, and superior, scalable services than Azure. More significantly, while Azure was attempting to catch up, Amazon could expand its cloud infrastructure by adding more servers and utilizing economies of scale more effectively.Which is the best platform to learn cloud computing? ›
- Microsoft (Microsoft Azure)
- Amazon (Amazon Web Service)
- Google (Google Cloud Platform)
- IBM (IBM Cloud)
Keras. Francois Chollet originally developed Keras, with 350,000+ users and 700+ open-source contributors, making it one of the fastest-growing deep learning framework packages. Keras supports high-level neural network API, written in Python.
The neural network consists of three layers: an input layer, i; a hidden layer, j; and an output layer, k.
1. TensorFlow. TensorFlow is one of the most popular, open-source libraries that is being heavily used for numerical computation deep learning.Which cloud platform is best in 2022? ›
- Amazon Web Service (AWS)
- Google Cloud Platform.
- Microsoft Azure.
- IBM Cloud.
After Microsoft Azure and AWS, the Google Cloud platform is the next most popular cloud service provider. Like other major cloud hosting platforms, Google Cloud allows Solutions Architects to build solutions using computation, machine learning (ML), storage, networking, and the internet of things (IoT).Which cloud platform is growing fastest? ›
IaaS is reported as the second largest spending category and is the fastest growing with a projected five-year CAGR of 32.0%. PaaS is the lowest spending category, with the second largest five-year CAGR of 29.9%. For a more detailed look within the public cloud market we must turn to alternative research and reports.What are the top 10 online courses? ›
- Graphic Design.
- Cyber Security.
- Data Analytics.
- Digital Marketing.
- Social Media Marketing.
- Foreign Language.
- Web Development.
- Kahoot! Gamified lessons to boost student engagement. Age group: 5-18. ...
- Scratch. Coding for budding programmers and problem-solvers. Age group: 8-16. ...
- Flipgrid. Social learning to empower student voice. ...
- Calm. Student well-being and social-emotional learning.
- The Best Learning Management Systems of 2022.
- MATRIX LMS.
- D2L Brightspace LMS.
- Blackboard Learn LMS.
We've compiled a summary cloud services comparison of Amazon Web Service (AWS), Microsoft Azure, and Google Cloud Platform (GCP) to help inform you in your multi-cloud journey.Which 3 platforms dominate the cloud services market? ›
The Top 5 Cloud Market Share Leaders
Spending on cloud infrastructure services reached nearly $55 billion in the second quarter of 2022 with Amazon Web Services, Microsoft Azure and Google Cloud leading the way.
Microsoft Three Clouds - Azure, Dynamics 365, Microsoft 365 | Cambay.Is AWS losing to Azure? ›
Long the leader, AWS appears to be losing ground to competitor Azure, at least per IDC. Microsoft Azure surpassed Amazon Web Services in 2021, according to new public cloud numbers from IDC.Does Nike use AWS or Azure? ›
Nike relies on AWS to provide personalized apps, better features, up-to-date content and responsive shopping experience for its customers.Is Azure beating AWS? ›
Azure pulls in front of AWS in public cloud adoption
The key takeaway on the Azure front is its leadership with enterprise users, with 80 percent of respondents adopting Microsoft's public cloud, up from 76 percent the previous year.
If you want a cloud engineer job, you can follow AWS education for 3 months. If you are new to the IT world and know nothing about computer science and the cloud, you can follow the Basic computer training for 3 months before the AWS training. ITF courses should be taken if you have no experience in the IT world.Which cloud certification has highest demand? ›
The AWS Solutions Architect credential is among the most highly-valued and challenging credentials available today. It is a professional-level credential with a professional difficulty level to the exam. AWS or Amazon Web Services is an Amazon subsidiary offering cloud products and solutions for enterprises.Which platforms are used for cloud computing? ›
Most major cloud service providers — including Amazon Web Services (AWS), Google Cloud, IBM Cloud and Microsoft Azure — offer IaaS with their cloud computing services.What is the largest deep learning model? ›
GPT-3's deep learning neural network is a model with over 175 billion machine learning parameters. To put things into scale, the largest trained language model before GPT-3 was Microsoft's Turing NLG model, which had 10 billion parameters. As of early 2021, GPT-3 is the largest neural network ever produced.Does Netflix use deep learning? ›
Netflix uses machine learning to analyze your movie and series choices and understand what sort of thumbnail you are most likely to click.Which database is best for deep learning? ›
- Apache Cassandra.
What Are the Components of a Neural Network? There are three main components: an input later, a processing layer, and an output layer. The inputs may be weighted based on various criteria.How many types of deep learning are there? ›
Different types of Neural Networks in Deep Learning
This article focuses on three important types of neural networks that form the basis for most pre-trained models in deep learning: Artificial Neural Networks (ANN) Convolution Neural Networks (CNN) Recurrent Neural Networks (RNN)
TensorFlow is an open-sourced end-to-end platform, a library for multiple machine learning tasks, while Keras is a high-level neural network library that runs on top of TensorFlow. Both provide high-level APIs used for easily building and training models, but Keras is more user-friendly because it's built-in Python.Does Tesla use PyTorch or TensorFlow? ›
Even Tesla is using PyTorch to develop full self-driving capabilities for its vehicles, including AutoPilot and Smart Summon. It is very easy to try and execute new research ideas in PyTorch; for example, switching to PyTorch decreased our iteration time on research ideas in generative modeling from weeks to days.What deep learning model does Tesla use? ›
Training using PyTorch
The Tesla tech stack uses PyTorch for training purposes of the deep learning model.
2. Edge Computing. Cloud providers are moving closer to the edge to respond to the growth of 5G, Internet of Things (IoT) devices, and latency-sensitive applications. Edge computing is not new to the tech industry glossary, but companies are increasingly adopting it.What are the 6 most common cloud services? ›
In terms of Services AWS is the clear winner, as the amount of services offered by AWS is way more than offered by GCP. Services available on AWS is extremely broad and wide. These various services are really well integrated, and they provide a very comprehensive cloud service. Learn more from the AWS Cloud Course.Which technologies will dominate in 2022? ›
Genomics, gene editing, and synthetic biology are a top trend of 2022 because these advancements can help us modify crops, cure and eradicate diseases, develop new vaccines like the COVID-19 shot, and other medical and biological breakthroughs.What technology will replace the cloud? ›
With edge computing, data that is produced by IoT devices is processed closer to where it's created, instead of being sent across long routes to data centers or the cloud.
One distributed application that users are becoming increasingly familiar with is blockchain – the distributed data storage format that underpins cryptocurrency and NFTs. Some are suggesting that the encrypted and secure nature of blockchain makes it a good fit for creating new models of cloud computing infrastructure.Which is the 2nd largest cloud provider? ›
The Alibaba Cloud is the second-largest cloud service provider around the world and powers almost half of China's 4.97 million websites.Is Azure or AWS growing faster? ›
Azure's growth rate is estimated to be 18.5% in 2019, while AWS' growth rate is estimated to be 13. PRO TIP: Please be advised that Azure is growing faster than AWS and that this may have an impact on your business.Which cloud is better AWS or Azure? ›
Azure is almost an evergreen comparison since AWS and Microsoft Azure are the two most established leading names in the cloud computing space. As of now, AWS is bigger in terms of revenue while Azure has a bigger user base.Is 8 GB RAM enough for deep learning? ›
The average memory requirement is 16GB of RAM, but some applications require more memory. A massive GPU is typically understood to be a "must-have", but thinking through the machine learning memory requirements probably doesn't weigh into that purchase.Is Nvidia GeForce good for deep learning? ›
Best Deep Learning Workstations
The general rule is this: the best deep learning workstation should be equipped with the best GPU available today, for example, an NVIDIA GeForce RTX 3090, RTX 3080, RTX 3070, RTX A6000, RTX A5000, or RTX A4000.
Build your deep learning project fast on Google Cloud
Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility.
- Python. Over the years, the use of Python has been growing steadily, overtaking popular languages like Java, C, C++, and C#. ...
- R. ...
- Java. ...
While the number of GPUs for a deep learning workstation may change based on which you spring for, in general, trying to maximize the amount you can have connected to your deep learning model is ideal. Starting with at least four GPUs for deep learning is going to be your best bet.Is RAM or GPU more important for deep learning? ›
Training a model in deep learning requires a large dataset, hence the large computational operations in terms of memory. To compute the data efficiently, a GPU is an optimum choice. The larger the computations, the more the advantage of a GPU over a CPU.
RAM Size. RAM size does not affect deep learning performance. However, it might hinder you from executing your GPU code comfortably (without swapping to disk).Which GPU is better for deep learning? ›
NVIDIA's RTX 3090 is the best GPU for deep learning and AI in 2020 2021. It has exceptional performance and features make it perfect for powering the latest generation of neural networks. Whether you're a data scientist, researcher, or developer, the RTX 3090 will help you take your projects to the next level.Is AMD or Intel better for deep learning? ›
Intel is your best choice for a higher frequency clock speed, and AMD is your best hope for a greater thread count. When it comes right down to it, these factors are only a few worth considering, but they are possibly more important to machine learning directly.Is AWS better than Google Cloud? ›
In terms of Services AWS is the clear winner, as the amount of services offered by AWS is way more than offered by GCP. Services available on AWS is extremely broad and wide. These various services are really well integrated, and they provide a very comprehensive cloud service. Learn more from the AWS Cloud Course.Is Azure better than Google Cloud? ›
Azure vs Google Cloud: Storage Services
Summary: Azure provides a well-rounded set of storage services and features, but can have a steep learning curve, especially for users without a background in Microsoft technology. Google offers fewer features but shines in storage pricing and ease of its use.
If you want to enter an entry-level position in a cloud-related job, you'd have a better chance if you choose to learn AWS as more job offerings seek expertise in AWS. And if you are interested in specialized fields like Big Data and Machine learning in the cloud, then consider looking into GCP.What is the most popular deep learning framework? ›
Ever since it was released, TensorFlow has become the most popular deep learning framework. TensorFlow's flexible architecture allows you to build custom deep learning models and use its components to develop new machine-learning tools.