Machine Learning (ML) is a subset of artificial intelligence that emulates human learning, allowing machines to improve their predictive capabilities until they can perform tasks autonomously, without specific programming. ML-driven software applications can predict new outcomes based on historical training data.
Training an accurate ML model requires large amounts of data, computing power, and infrastructure. Training a machine learning model in-house is difficult for most organizations, given the time and cost. A cloud ML platform provides the compute, storage, and services required to train machine learning models.
Cloud computing makes machine learning more accessible, flexible, and cost-effective while allowing developers to build ML algorithms faster. Depending on the use case, an organization may choose different cloud services to support their ML training projects (GPU as a service) or leverage pre-trained models for their applications (AI as a service).
This is part of an extensive series of guides about machine learning.
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Many organizations are capable of building machine learning models in-house, using open source frameworks such as Scikit Learn, TensorFlow, or PyTorch. However, even if in-house teams are capable of building algorithms, they will often find it difficult to deploy models to production and scale them to real-life workloads, which often requires large computing clusters.
There are several barriers to entry for deploying machine learning capabilities into enterprise applications. The expertise required to build, train, and deploy machine learning models adds to the cost of labor, development, and infrastructure, along with the need to purchase and operate specialized hardware equipment.
Many of these problems can be addressed by cloud computing. Public clouds and AIaaS services help organizations leverage machine learning capabilities to solve business problems without having to undertake the technical burden.
The key benefits of cloud computing for machine learning workloads can be summarized as follows:
Machine learning in the cloud has three key limitations:
Artificial Intelligence as a Service (AIaaS) is a delivery model that enables vendors to provide artificial intelligence (AI) that reduces their customer’s risk and initial investment. It helps customers experiment with various cloud AI offerings and test different machine learning (ML) algorithms, using the services that suit their scenario best.
Each AIaaS vendor offers various AI and machine learning services with different features and pricing models. For example, some cloud AI providers offer specialized hardware for specific AI tasks, like GPU as a Service (GPUaaS) for intensive workloads. Other services, like AWS SageMaker, provide a fully managed platform to build and train machine learning algorithms.
GPU as a Service (GPUaaS) providers eliminate the need to set up on-premises GPU infrastructure. These services let you elastically provision GPU resources on demand. It helps reduce the costs associated with in house GPU infrastructure, increase the level of scalability and flexibility, and enable many to implement large-scale GPU computing solutions at scale.
GPUaaS is often delivered as SaaS, ensuring you can focus on building, training, and deploying AI solutions to end users. You can also use GPUaaS with a server model. Computationally intensive tasks consume massive amounts of CPU power. GPUaaS lets you offload some of this work to a GPU to free up resources and improve performance output.
Here are three popular machine learning platforms offered by the leading cloud providers.
SageMaker is Amazon’s fully managed machine learning (ML) service. It enables you to quickly build and train ML models and deploy them directly into a production environment. Here are key features of AWS SageMaker:
Azure Machine Learning is a cloud-based service that helps accelerate and manage the entire ML project lifecycle. You can use it in workflows to train and deploy ML models, create your own model, or use a model from sources like Pytorch or TensorFlow. It also lets you manage MLOps, ensuring you can monitor, retrain, and redeploy your models.
Individuals and teams can use this service to deploy ML models into an auditable and secure production environment. It includes tools that help automate and accelerate ML workflows, integrate models into services and applications, and tools backed by durable Azure Resource Manager APIs.
AutoML is Google Cloud’s machine learning service. It does not require extensive knowledge of machine learning. AutoML can help you build on Google’s ML capabilities to create custom ML models tailored to your specific needs. It lets you integrate your models into applications and websites. Here are key features of AutoML:
Extract, Transform, Load (ETL) and Extract, Load, and Transform (ELT) are two common data pipeline models. Machine learning and deep learning amplify the need for data transformation to meet the specific requirements of ML models. ELT gives you more flexibility if you need to change transformations midway. This is commonly needed in the load phase, which is the most time-consuming in many big data projects.
When training large-scale models, it can be very useful for notebooks to have access to multiple large virtual machines or containers. Training can greatly benefit from accelerators such as GPUs, TPUs, and FPGAs. A cloud machine learning platform should provide access to these resources at an affordable cost.
Most data scientists have a preferred machine learning and deep learning framework and programming language:
Cloud machine learning and deep learning platforms tend to provide their own algorithms and prepackaged models, with support for certain external frameworks, or containers with specific entry points. Evaluate if the platform will let you integrate algorithms you have built using the framework of your choice with its native AutoML capabilities.
Cloud machine learning platforms provide optimized AI services for use cases like computer vision, natural language processing, speech synthesis, and predictive analytics. These services are typically trained and tested using more data than is available to most businesses. They are deployed on service endpoints with sufficient compute resources, including hardware accelerators, to ensure excellent response times and high scalability.
Cloud-based machine learning platforms should provide the tools to monitor model performance and respond to changes. Models that provided excellent performance at first can degrade in performance over time due to changes to data inputs. The platform should provide observability capabilities that let you identify performance issues and understand their root cause, allowing you to tune the model or retrain it on a more relevant dataset.
Follow these five steps to train your machine learning project in the cloud.
Sort through your data and identify the sources—this could be a complicated and time-consuming process, especially if you have incomplete data. If you need to move data from on-premises environments to the cloud, take into account data transfer rates in case of large data volumes, and check for any compliance or legal restrictions.
It is important to provision the appropriate storage resources to store your dataset and compute resources to process it.
Start your modeling process using iterative steps. First, conduct feature engineering to determine the variables you want to model. Next, start training the model. Feature engineering is a complicated but critical process and requires business and domain knowledge for exploratory data analysis. One challenge is to ensure you have the right number of variables to enable the model’s functionality while avoiding noise.
Model training is a standard procedure with iterative testing and training steps. Cloud-based machine learning is useful for testing multiple machine learning models, given the flexibility of cloud computing resources. The algorithms you use depend on your business requirements, data accuracy requirements, data volume and availability, parameters, and the computing task (i.e., classification, prediction).
Cloud providers offer automated machine learning services that let you tune hyperparameters and test multiple algorithms simultaneously. For example, Azure offers AutoML, which supports different ensemble modeling methods and incorporates best practices for building an ML model. It also provides a centralized workspace to keep track of your artifacts, including the full model history.
Once you’ve built a model that meets your business objectives, you can deploy it at scale. Once you have trained the model using a cloud-based ML platform, deployment should be straightforward. This typically involves defining the model endpoint, specifying computing resources that should run the model, and hitting the switch.
When you deploy your model, you must monitor it continuously to ensure it functions properly. Monitor performance to verify if the model’s predictions are relevant and accurate. Some cloud ML platforms offer automated data drift monitoring. Look out for data drift to keep the predictions relevant (the input data can diverge from the training data over time). When data drift occurs, revisit your dataset and retrain the model with more relevant data.
When running machine learning in the cloud at scale, you’ll need to manage a large number of computing resources and GPUs. 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.
Here are some of the capabilities you gain when using Run:AI:
Run:AI simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.
Learn more about the Run.ai GPU virtualization platform.
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 machine learning.
Authored by Run.AI
Authored by Datagen
Authored by Datagen