Understanding AI Infrastructure and Google Cloud as an Industry Leader
The rate of AI adoption has accelerated with the shift into the Industrial Revolution 4.0, but this enhanced rate is projected to generate substantial demand for computing resources and associated infrastructure.
In Appen’s 2022 State of AI survey, 54% of organisations in the US claim to be ahead of their counterparts in adopting AI in their industry, while 42% of them claim to be even. Within the region of the UK and Europe, 44% claim to be ahead while 51% claim to be even. The statistical proof from this survey shows the undeniable growth of AI adoption and will only grow to intensify further.
The Economic Times reported on the global revenues for the AI market in 2021, which includes hardware, software and services for both AI-centric and AI-non-centric applications, reaching a whopping (USD) $383.3 billion, totalling an increase of 20.7% over the year prior, based on the International Data Corporation (IDC). It is also forecasted that the AI market value is expected to peak at $450 billion in 2022 while maintaining a year-over-year growth rate over the next five years.
Businesses must be versatile when it pertains to infrastructure as the demand for large volumes of data grows further, making hybrid cloud computing the backbone of AI. By utilising a hybrid cloud, companies may fulfil the technical needs of AI at the optimal pricing range for their company and operations. Infrastructure-as-a-Service (IaaS) enables businesses to utilise, produce, and integrate AI without jeopardizing performance.
5 Points to Consider
Computing Power - To capitalise on the potential offered by AI, businesses must have access to high-performance computing resources including CPUs and GPUs. To complete a large number of computations, ML algorithms must be rapid and efficient. Although a CPU-based environment may manage basic AI tasks, deep learning necessitates the capacity to run scalable neural network algorithms across several big data sets. CPU-based computing may not be capable of achieving these goals, and GPUs may be a superior solution. When compared to CPUs, the higher performance offered by GPUs may hasten deep learning.
Networking Infrastructure - Another critical component of AI infrastructure is networking. To maximise the delivery of results, efficient, quick, and dependable networks are required. Because deep learning algorithms rely on communication, networks must keep up with demand as AI initiatives develop. Scalability is key, and AI necessitates a high-bandwidth, low-latency network. The service wrap and technology stack must be uniform across all locations.
Storage Capacity - Many organisations rely on the capacity to increase storage as data volumes grow. Organisations must choose what kind of storage they need, and there are several things to consider, including the level of AI they want to deploy and if they require real-time judgements. For instance, a FinTech business that uses AI algorithms for real-time trading decisions may need fast all-flash storage technology, but other organisations may enjoy higher capacity but slower storage. Subsequently, businesses must estimate how much data their AI applications will produce since AI applications perform better when presented with more data.
Security Because AI might include managing sensitive information such as health records, financial details, and private details, the infrastructure must be secured end-to-end using cutting-edge technology. A security breach would be disastrous for any firm, but with AI, any injection of faulty data might cause the algorithm to draw wrong conclusions, resulting in poor choices.
Cost-effectiveness While AI models become more complicated, they become increasingly more costly to run, therefore grinding out extra efficiency from the infrastructure is crucial to keeping costs in check. As businesses grow their use of AI, the server, network and storage infrastructures will experience more strain. Businesses must make cautious decisions and choose IaaS suppliers that can supply cost-effective dedicated servers to improve performance and continue making investments in AI without raising their expenditures.
Google Cloud as the Leader in AI Infrastructure
Google Cloud has been rated a Leader in Forrester Research's “The Forrester Wave: AI Infrastructure”, Q4 2021 report, produced by Mike Gualtieri and Tracy Woo. Forrester examined AI architecture, training, inference, and management against a set of pre-defined criteria in the report. In 16 different Forrester Wave evaluation criteria, Google Cloud achieved the best possible score. Forrester’s assessment and acknowledgement provide clients with the absolute confidence they require to make key platform choices that will have enduring strategic value.
Google Cloud provides consumers with a diverse collection of key components, including Deep Learning VMs and containers, the most recent GPUs/TPUs, and a marketplace of vetted ISV products to assist in the design of your unique software stack on VMs and/or Google Kubernetes Engine (GKE).
Google Cloud offers GPU and TPU accelerators for a variety of applications, namely high-performance training, low-cost inference, and large-scale accelerated data processing. Google is the sole public cloud provider that offers up to 16 NVIDIA A100 GPUs in a single VM, allowing incredibly large AI models to be trained on a single node. Users may begin with a single NVIDIA A100 GPU and expand up to 16 GPUs without the need to configure numerous VMs for single-node ML training. TPU pods are also available from Google for large-scale AI research using PyTorch, TensorFlow, and JAX. The updated fourth-generation TPU pods achieve exaflop-scale peak performance, outperforming previous MLPerf benchmarks with a 480 billion parameter language model.
Google Kubernetes Engine offers the most sophisticated Kubernetes features, including Autopilot, highly automated cluster version upgrades, and cluster backup/restore. Given its capability for 15,000 nodes per cluster, auto-provisioning, auto-scaling, and a variety of machine types, GKE is an excellent choice for a scalable multi-node bespoke platform for training, inference, and Kubeflow pipelines. GKE additionally supports dynamic scheduling, coordinated maintenance, high availability, job API, customisability, fault tolerance, and ML frameworks, which help ML workloads. When a company's footprint expands to include a fleet of GKE clusters, its data teams may use Anthos Config Management to ensure uniform settings and compliance with security policies.
Security is built into the Google Cloud stack through progressive levels that provide defence in depth. Measures such as boot-level signature and chain-of-trust validation are employed to achieve data security, authentication, authorisation, and non-repudiation. With consumers in control, ubiquitous data encryption provides unified control over data at rest, in use, and in transit. Choices are provided for running in fully encrypted confidential settings utilising managed Hadoop or Spark with Confidential Dataproc or Confidential VMs.
Google Cloud collaborates with qualified partners throughout the world to assist clients in designing, implementing, and managing sophisticated AI systems. The increasing list of Google Cloud collaborators with ML specialisations that have exhibited customer success across sectors, including strong ties with the leading Global System Integrators, continuously grows longer. The Google Cloud Marketplace also includes a list of technology partners that enable businesses to deploy ML applications on Google Cloud's AI infrastructure.
Typically, even for highly trained data scientists with complex systems, developing well-tuned and effectively maintained ML systems has been problematic. Organisations can now create, deploy, and scale ML models quicker using pre-trained and custom tools inside a unified AI platform thanks to the major pillars of Google's contributions listed above. Google Cloud hopes to keep innovating and assisting customers on their digital transformation adventure.