Machine Learning in the Cloud: Benefits and Limitations

Benefits of Machine Learning in the Cloud

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:

• On-demand pricing models make it possible to embark on ML initiatives without a large capital investment. 
• The cloud provides the speed and performance of GPUs and FPGAs without requiring an investment in hardware. 
• The cloud allows businesses to easily experiment with machine learning capabilities and scale as projects move into production and demand for those capabilities grows.
• The cloud allows access to ML capabilities without advanced skills in artificial intelligence or data science.

What Are the Limitations of Machine Learning in the Cloud?

Machine learning in the cloud has three key limitations:

• Doesn’t replace experts—ML systems, even if they are managed on the cloud, still require human monitoring and optimization. There are practical limits to what AI can do without human oversight and intervention. Algorithms do not understand everything about a situation and do not know how to respond to every possible input.
• Data mobility—when running ML models in the cloud, it can be challenging to transition systems from one cloud or service to another. This requires moving the data in a way that doesn't affect model performance. Machine learning models are often sensitive to small changes in the input data. For example, a model may not work well if you need to change the format or size of your data.
• Security concerns—cloud-based machine learning is subject to the same concerns as any cloud computing platform. Cloud-based machine learning systems are often exposed to public networks and can be compromised by attackers, who might manipulate ML results or run up infrastructure costs. Cloud-based ML models are also vulnerable to denial of service (DoS) attacks. Many of these threats do not exist when models are deployed behind a corporate firewall.

Types of Cloud-Based Machine Learning Services

Artificial Intelligence as a Service (AIaaS)

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)

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.

Popular Cloud Machine Learning Platforms

Here are three popular machine learning platforms offered by the leading cloud providers.

AWS SageMaker

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:

• An integrated Jupyter authoring notebook instance—provides easy access to data sources for analysis and exploration. There is no need to manage servers. 
• Common machine learning algorithms—the service provides algorithms optimized for running efficiently against big data in a distributed environment. 
• Native support for custom algorithms and frameworks—SageMaker provides flexible distributed training options designed to adjust to specific workflows. 
• Quick deployment—the service lets you use the SageMaker console or SageMaker Studio to quickly deploy a model into a scalable and secure environment.
• Pay per usage—AWS SageMaker bills training and hosting by usage minutes. There are no minimum fees or upfront commitments.

Azure Machine Learning

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.

Google Cloud AutoML

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:

• Vertex AI—unifies AutoML and AI Platform into one user interface, API, and client library. It lets you use AutoML training and custom training, save and deploy models, and request predictions.
• AutoML Tables—allows an entire team to automatically build and deploy machine learning (ML) models on structured data at scale.
• Video Intelligence—this feature provides various options to integrate ML video intelligence models into websites and applications.
• AutoML Natural Language—this feature uses ML to analyze the meaning and structure of documents, allowing you to train a custom ML model to extract information, classify documents, and understand authors’ sentiments.
• AutoML Vision—lets you train ML models to classify images according to your own custom labels.

How to Choose a Cloud Machine Learning Platform

Support for ETL or ELT Pipelines

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.

Support for Scale-Up and Scale-Out Training

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.

Support for Machine Learning Frameworks

Most data scientists have a preferred machine learning and deep learning framework and programming language: 

• For Python users, Scikit-learn is often the best choice for machine learning. 
• Deep learning models are most commonly developed with TensorFlow, PyTorch, or MXNet. 
• Scala users commonly use SparkMLlib. 
• R is an aging framework but it has many basic machine learning packages and is still commonly used by many data scientists. 
• In the Java world, the H2O.ai framework is a common choice.

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.

Pre-Tuned AI Services

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.

Monitor Prediction Performance

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.

Training Machine Learning Projects in the Cloud

Follow these five steps to train your machine learning project in the cloud.

Identify and Understand Your Data Sources

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. 

Engineer the Features

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.

Train and Validate Your Model 

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.

Deploy and Monitor Your Model 

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.