Artificial Intelligence Projects Can Benefit From The Multi-Cloud

Why organizations should consider a multi-cloud strategy for their AI/ ML projects and how to make it happen


As organizations migrate their Artificial Intelligence (AI) solutions to the cloud, others have already embarked on AI or Machine Learning (ML) multi-cloud strategies. There are significant advantages to adopting a multi-cloud strategy — using several public cloud providers to manage your infrastructure and applications — from driving AI-driven business solutions at lower costs to gaining flexibility. But adopting a multi-cloud strategy is a complex undertaking.


Let’s look in more detail at some of the advantages of embracing a multi-cloud strategy for your AI and ML projects; point out some of the challenges, and highlight an approach designed to overcome these challenges and drive success.


The advantages of a multi-cloud AI strategy


Companies adopting a multi-cloud strategy can avoid being locked in with a single cloud service provider. All the Intellectual Property an organization develops (the AI or ML models, analytics, the processes, the rules, the applications, and even the database) are locked in and dependent on the cloud provider’s infrastructure and products. This makes it extremely difficult to migrate your Intellectual Property from one service provider to another.


Once you’ve built an application that leverages a cloud provider’s many products, it can be costly and difficult to reconfigure this application to run natively on another cloud. For example, some specific products offered by AWS® cannot work out of the box on Microsoft® Azure or Google Cloud Platform®. So, migrating to another service provider may entail rebuilding your application’s functionality using another service provider’s equivalent product, integrating a comparable 3rd party payable product, or using open-source; or doing away with that functionality altogether. Transferring your data from one cloud to another can also be extremely challenging.


The multi-cloud allows organizations to match each cloud service provider’s particular offerings to their specific AI data needs and application process requirements. For example, an organization’s data scientists could easily scale their storage capacity up or down and optimize the computing power to run their ML algorithms using a particular cloud service provider. On the other hand, the application developers may choose to deploy their applications on another cloud that hosts their favorite database or which is better suited to handle a specific consumer activity workload.


Bandwidth and latency are important factors to consider when choosing your cloud strategy. This is especially important for use-cases where a fast response time is critical for business success. For example, a license plate recognition application that analyzes thousands of license plates in seconds can’t afford any latency. Similarly, a manufacturing production line using AI image recognition for product quality control also needs fast response time to avoid product defects. For use-cases such as these, accessing geographically dispersed cloud providers offers businesses the possibility to leverage proximity to reduce latency and lower bandwidth costs.


Running your AI projects over multi-cloud services providers offers an extra level of reliability, further reduces the risks of downtime, and provides organizations with the business continuity their business use-cases require. Although cloud service providers offer different levels of recovery and redundancy and rarely encounter infrastructure meltdowns these days, accidents can still happen. Organizations’ business operations can still be severely damaged by a cloud outage, especially if they’re mission-critical. Suppose one of your cloud providers suffers from an outage. Running your AI applications on several different clouds allows you to quickly (in some cases automatically) switch your AI application to another cloud provider and experience no downtown at all.


There are several ways that a multi-cloud strategy can give you this extra level of flexibility. You can choose to have different cloud providers in the same region (or close by) or different cloud providers across several regions. To be fair, companies can also address these high-availability issues with other topologies that are not multi-cloud vendor-based. They can opt to use a single cloud provider in a single geographical region, but with different availability zones (generally speaking, an availability zone is considered a single data center), or use a single cloud provider spread out over different geographical regions.


The challenges of a multi-cloud AI strategy


However, operating multi-cloud AI projects creates multiple operational and management challenges. Coming up to speed on a single cloud platform takes specialized and dedicated resources and a lot of training. Managing AI operations on multiple clouds increases the complexity tenfold. It entails hiring multi-cloud experts and investing even more in training. It also requires well-organized cross-functional teams to set up, monitor, optimize, and secure their AI applications across multiple clouds.


Each cloud service provider has different infrastructures, requirements, specifications, and security set-up procedures, which quickly makes managing these different cloud infrastructures an immense task. When changes occur, settings need to be manually updated on each cloud.


Few organizations can afford the expertise and the expenses needed to manage a multi-cloud environment. Even when they can, they encounter the next obstacle: a general shortage of machine learning and cloud infrastructure experts. As a result, many organizations do not have the in-house expertise required to create and manage multi-cloud AI solutions and find themselves struggling to keep up with the Joneses.


Matching the cloud provider with the lowest price for a particular job is also an advantage. But managing several cloud provider pricing models, Service Level Agreements (SLAs), and contracts can quickly become a nightmare.


An Easier Multi-Cloud Solution


Businesses with these challenges should consider a cloud-based DataOps platform with MLOps capabilities to manage their AI and ML projects over multiple cloud infrastructures. Most of these platforms offer a central framework and interface from which to set up, deploy, monitor, provision, and secure an organization’s ML, AI, and Analytics applications while managing multi-cloud, interoperability, and scalability — hence offering organizations the ultimate and much-needed agility for their AI projects. Training and adoption are also highly simplified.


There are a lot of DataOps platforms with MLOps capabilities in the market today. The better ones enable a unified ML approach (the MLOps part) that runs seamlessly across any cloud environment, whether public or private and do a lot of the cloud infrastructure management work for you (the DataOps part). They automatically set up, provision, and maintain your own environments in the different clouds, making it simple for an organization to adopt a multi-cloud strategy and fine-tune it as business and application needs change.


The more advanced platforms maintain consistent data, processes, business rules, and application instances across the multi-cloud, enabling organizations to achieve complete agility and workload mobility between heterogeneous cloud platforms. As data grows, these platforms properly store the data where it needs to be, secure it, and ensure that it is always ready for the multiple AI and even Analytics projects an organization might have.


As we’ve seen above, ensuring that your security settings are set correctly for each cloud provider, project, and data type is a formidable task. A simpler way is to manage your security settings in one central place. The platform you choose should automatically reflect these settings and the changes throughout the cloud infrastructures for you.


These platforms can help organizations save on their cloud expenses. Organizations should not have to shop around for the best available rates, understand the different pricing options and worry about flexible contracts, payment flexibility, and services to secure the best deals based on their specific needs. Look for a platform that provides a clear and consistent low price across all your cloud infrastructures. This pricing structure will reflect your organization’s needs and only evolves as these needs increase.


There’s a tradeoff to be aware of, however. Although you gain in flexibility the higher up you move away from the service provider infrastructure, you do so at the cost of losing in functionality. These platforms offer fewer products and functionality than the service providers themselves.


When choosing a multi-cloud versus a single-cloud or a multi-cloud MLOps strategy, organizations should carefully weigh the advantages and disadvantages of each approach and think about how their needs might evolve over time to avoid migration headaches expenses and be better prepared for the future.


In this article, we focused on public multi-cloud and not the hybrid cloud which includes public and private clouds. Companies adopt hybrid-cloud strategies for different reasons than public multi-cloud alone. However, the hybrid cloud presents many similar issues to the public multi-cloud; they are often more complex.


The original article was posted by Paul Sinaï on Towards Data Science.