Why a multi-cloud strategy paves the way to successful Machine Learning and Analytics projects with lower costs and more reliably. How to implement platform independence.
As organizations migrate their Machine Learning projects 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 ML 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 ML 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.
High bandwidth and low latency in the multi-cloud
Bandwidth and latency are important factors to consider when choosing your multi-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 ML 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.
How the multi-cloud fosters reliability and business continuity
Running your ML 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 ML applications on several different clouds, by adopting a multi-cloud strategy allows you to quickly (in some cases automatically) switch your ML application to another cloud provider and experience no downtown at all.
How the multi-cloud provides extra flexibility
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 ML strategy
However, operating multi-cloud ML 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 ML operations on multiple clouds increases the complexity tenfold. It entails hiring multi-cloud experts and investing even more in training. A multi-cloud strategy requires well-organized cross-functional teams to set up, monitor, optimize, and secure their AI applications across multiple clouds.
This blog post is an excerpt an article posted by Paul Sinaï on Towards Data Science. Continue reading the original article.
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