Cloud strategies in the era of AI
In today’s world, people interact daily with software and digital services by means of the cloud. And the cloud allows developers to provide cost-effective projects, reliable apps, and secure solutions. But now, in the era of AI, exploring multi-cloud strategies is vital. Implementing a multi-cloud strategy allows a company to optimize its spending costs and be more efficient. And using several public cloud providers allows for better speed and performance.
As companies migrate their ML and AI projects to the cloud, migrations can be a complex undertaking and need to be well thought out, so sticking to a single cloud solution often seems the most affordable and reliable solution. On the other hand, there are many advantages to adopting such a multi-cloud strategy. In this article, we will discuss the challenges of vendor lock-in with single cloud vendors and the importance of multi-cloud deployment for AI/ML. We will look at the currently available solutions in the market and discover what the future holds.
Single cloud vs multi-cloud
As said, companies historically chose single cloud providers to meet their infrastructure needs. And this makes perfect sense. Single cloud providers taken singularly appear to offer the best management potential. Outsourcing applications and workloads to a single cloud provider is the easiest way to manage a process. Privacy and control would also appear to be easier to maintain.
Yet now, the industry is starting to shift towards a multi-cloud approach. But why would we move away from the easier management model? Why migrate the workload between multiple cloud providers? The answer becomes evident when we look at the individual benefits of each vendor. And even more so, when we look at the individual shortcomings of any vendor.
Importance of developing a multi-cloud strategy for infrastructure
We can define multi-cloud as when a company uses cloud computing services from at least two cloud providers. Adopting a multi-cloud strategy for AI/ML infrastructure affords a company huge freedom. Choosing from multiple cloud service providers adds more stability. You can leverage the unique services offered by the individual cloud service providers. That can help reduce the risk of service disruptions.
In fact, even a short spell of downtime can cause millions of dollars of revenue loss. Facebook’s six-hour outage in 2021 cost them an estimated $80m in ad revenue. And the loss to Mark Zucherberg’s net worth was estimated to be over $7 billion. A well-implemented multi-cloud strategy can ensure your applications run 24/7. This enhanced reliability reduces the risk to your infrastructure and applications.
Mitigating the effect of vendor lock-in through multi-cloud strategy
But the single greatest benefit to adopting a multi-cloud strategy relates to vendor lock-in. When a company is excessively dependent on a single cloud provider, flexibility is lost. Your entire ecosystem depends on the services and reliability of one provider. You are bound by the processes and rules of a single entity. And if that provider decides to change its pricing or policies, you are in a delicate position.
Your dependency limits your freedom and also hinders your ability to switch providers easily. It can be costly to switch an application to run natively on another cloud. Adopting a multi-cloud strategy, you mitigate these risks associated with vendor lock-in. Having your workloads distributed across multiple clouds allows you to match a provider to your needs. Your AI/ML solutions avoid getting trapped within a single provider’s ecosystem.
Interoperability challenges and solutions in multi-cloud environments
Despite the overwhelming arguments, seen in the previous paragraphs, that support a multi-cloud environment, there are obviously also some downsides. A multi-cloud environment can present interoperability challenges that need consideration. The benefits that a single cloud strategy offers still stand. We already saw that a single cloud offers greater security with data and access permissions. While a multi-cloud system can be just as secure, the implementation overheads will be higher.
Additionally, configuration, migration, and maintenance processes vary from each vendor. This added complexity will require extra management. And the more providers you use, the greater this management task becomes. Different clouds use their own proprietary technologies and APIs. Seamless integration and data transfer between providers will require careful pre-planning. If a multi-cloud strategy is poorly implemented, the results could be catastrophic.
Why infrastructure-independent solutions are best for AI/ML
As for Artificial Intelligence projects, infrastructure-independent solutions offer many advantages to harness the true potential of AI. By separating AI/ML workloads into different cloud infrastructures, the possibilities are endless. You can select the optimal environment for each aspect of your application.
With the dramatic rise of AI and technologies such as ChatGPT, every business needs to keep up. Technology is evolving at an unprecedented rate. By choosing an infrastructure-independent solution for AI and ML, you can quickly adjust to new trends. You can transit operations between providers to get the optimum returns. This freedom allows a company to adjust its infrastructure as needed and get the best results. You can evolve at the same rate technology does. This ensures flexibility and future-proofing for your business.
Let’s see together a list of steps that will be beneficial for any implementer of AI multi-cloud solutions:
Step by Step: Multi-cloud deployment of AI
- Building and deploying the application onto the customer’s environment.
You should develop and build your AI application using the right AI/ML frameworks and tools. Once the application is ready, package it with any relevant dependencies. Now you can deploy the application onto the customer’s environment and configure the network settings. Make sure the application is fully compatible with the customer’s existing infrastructure.
- Containerization and open standard API.
You should place your AI application in a Docker container to isolate it from the physical infrastructure. Deploying your applications in separate containers brings many benefits. It will help reduce the risk of conflict between languages, libraries, and frameworks. It will also ensure portability and compatibility across different cloud providers. You should expose your application functionality through open standard APIs. This provides a standard communication interface for seamless integration across various cloud platforms.
- Customizing the deployment to the customer’s environment.
At this point, adapt the deployment of your AI application to suit the requirements of the customer’s environment. Configure all the network settings and include any specific constraints. You should take into account the storage requirements and relevant security access. When the application meets with the customer’s infrastructure and policies, move on to compatibility. Ensure the application is compatible across different cloud providers.
- Introducing abstraction through a framework and a Software Development Kit (SDK)
To make future work easier, consider introducing a framework and an SDK. This will help immensely in the deployment and management of your AI application. The framework will ensure that your application can be quickly deployed in a multi-cloud environment. The SDK contains the tools and libraries that developers can use to integrate with your AI application.
- In the future, a dedicated Integrated Development Environment (IDE) may be developed to make application development even easier.
As your AI application grows, so too will the complexity of your multi-cloud deployment. Therefore, you should consider developing a dedicated IDE. The IDE can hold a comprehensive set of instructions and tools to improve further integration. This will enhance developer productivity and allow for quickly adding new infrastructures. The resulting framework is pluggable and extendable.
Life is progressing at a pace we have never seen before. ML is not only changing the way we look at the world, it is transforming the world as we know it. Infrastructure-independent solutions in AI/ML will be at the center of this transformation.
Things that were once considered impossible, are becoming possible with ML. More and more organizations are embracing hybrid and multi-cloud architectures. And infrastructure-independent solutions are set to dominate and revolutionize the AI/ML market. Several key trends are coming to light that will shape the future of these solutions.
Being able to deploy AI/ML workloads across multiple cloud providers should become commonplace. With this, will come standardization and interoperability. As ML processing evolves, so too will the need for reliability and elimination of latency. Automation will help streamline updates and ensure seamless performance across multiple cloud environments.
By rising to the interoperability challenges that a multi-cloud strategy poses, companies like Abstract are shaping the future. We are on the path to unlock the full potential of AI/ML innovation. Developing a multi-cloud strategy for infrastructure-independent solutions in AI/ML is vital to this journey. Feel free to contact Abstract Srl to help you achieve this goal in the best way.