This is a guest post for the Computer Weekly Developer Network written by Mark Yi and is positioned as Director of Container Services, Alibaba Cloud Principal Engineer.
yi writes completely:
It is well known that cloud computing and artificial intelligence (AI) are fundamentally changing the behavior of business, but there is little attention paid to the role of containers fueled by this trend. As a portable and functional way to deploy applications, containers are a good way to meet the needs of businesses looking to make the most of new innovations.
Container technology is not new, but it is finding new value in the age of cloud computing and AI.
Originally introduced in the 1970s as a sandbox tool to help developers test their work without disrupting other services, it is now one of the most widely used technologies due to record demand for improved application development capabilities and increased expectations regarding computing efficiency.
Portability prioritization
Contension is popular as the growing number of microservice architectures require developers to prioritize application portability, efficient resource utilization, cost reductions, and standardized deployment processes.
Containers are great at disassembling large applications, allowing developers to update certain parts without overhauling the entire module. This means that if a particular application fails, it does not necessarily hinder the wider operations that support running.
This advantage combines the ability for developers to “write once, run anywhere” applications and containers, making them particularly suitable for supporting today's business's digital transformation ambitions.
Furthermore, the advent of serverless containers further simplifies the hardware side of application development. As enterprise applications become more complex, this deployment method separates workloads from the underlying hardware, allowing developers to focus on building their applications without worrying about configuring servers or devices.
Another aspect of improving efficiency lies in resource optimization.
The packaging of the application and its environment is a portable image. Companies can increase usage when needed, while saving overhead costs. Additionally, while cloud migration can be slow and costly otherwise, contentization can help encapsulate and deploy legacy applications in new cloud environments.
Container…and ai
The age of AI is opening new applications for container technology.
Examples are used to drive everything from autonomous vehicles to digital medical diagnosis and e-commerce recommendations.
The underlying machine learning and training models of these applications rely on specific libraries, frameworks and software, so standardized containers are ideal for addressing compatibility and version control issues. It also provides a high level of scalability for the enterprise and makes debugging easier by isolating it to lock in problems, while managing costs as training parameters grow. This is an advantage for streamlining the process from application development to deployment.
These benefits can be seen in the rapid increase in the use of this technology.
Already, almost 50% of AI deployments are using containers, and Gartner predicts this will rise to 75% by 2027.
In addition to this, AI is also used to improve container efficiency and performance. This is a true win-win. For example, cloud service providers provide AI container images with hardware acceleration libraries, AI runtimes and AI frameworks to meet deployment requirements in a variety of scenarios.
Consistent expectations
In many cases, new features are highly anticipated, and this is certainly the case with containers. Developers are looking for a seamless, scalable, cost-effective, safe solution, and can work quickly and efficiently.
As an example, the serverless delivery model prevents users from focusing too much on the underlying nodes and cluster management that they pay attention to for application development.
Another issue is standardization. This can be complicated when applying containers to AI workloads. Kubernetes' high technical barriers and complex businesses remain a bottleneck for many companies and developers interested in using containers. Innovations such as the Alibaba Cloud Container Compute Service (ACS) are trying to overcome this by incorporating cloud computing as the foundational layer of Kubernetes software. This allows businesses to move beyond mere container orchestration and experience a new paradigm with container computing.
Finally, using container computing technology for AI workloads increases agility, scalability, and cost-effectiveness for each deployment, while simplifying operational management. This fits well with DevOps best practices and cloud-native architectures, further enhancing the container as an ideal option for the deployment and management of complex AI systems.
Non-connected growth
Everywhere companies and developers see the value of containers. This explains the increase in adoption of this technology.
One example is Moka, a Chinese HR management software company as service software. This uses the maintenance-free nature of ACS clusters and focuses on core business priorities rather than infrastructure management. Additionally, on-demand scaling and pay-per user models provide Moka with the flexibility they need, especially during the peak recruitment season.
ACS is designed to make common enterprise-level workloads like web applications, CI/CD pipelines, big data computing tasks, and AI inference easier, more resilient and cost-effective. It also supports a wide range of scenarios for businesses in a variety of fields, including the Internet, gaming, retail, automobile, transportation, manufacturing, and more.
It's safe to say that the container technology market is merely expanding, especially given the explosive demand for AI applications. For those at the forefront of application development, it certainly is an exciting time to build.
