Although cloud computing and associated technologies are considerably more recent than artificial intelligence, AI has greatly benefited from them. A powerful catalyst has been cloud computing.
Hosting data and applications on the cloud enables businesses to be more flexible and nimble while reducing costs. This additional layer of AI adds intelligence to already-existing capabilities and provides great customer service by assisting in generating insights from the data. This results in a potently special mix that businesses can leverage to their advantage.
Data/datasets, processing power, especially GPUs, models/algorithms, and skills/talents can all be seen as dynamic influences that have shaped AI. Let’s examine how cloud computing has helped to advance and improve the components of AI:
Cloud delivery models
- IaaS (Infrastructure as a Service) delivery models on the cloud have made it easier for AI practitioners to access an infrastructure environment, including CPU, memory, disc, network, and operating system, without waiting for an infrastructure team to prepare it. Additionally, cloud service providers began offering GPU resources later.
- PaaS (Platform as a Service) enabled AI practitioners to quickly create next-generation applications using AI and data science technologies like Jupyter notebooks and data catalog services.
- SaaS (Software as a Service) allowed customers to incorporate AI services into applications like CRM and payment systems to produce effective outcomes.
Containers: As they began to separate apps from computing environments, containers gave all data scientists access to the same interface and setting. Additionally, data scientist teams are free to run their containers on several cloud providers, even if they prefer GPU capabilities.
Kubernetes: A system for automating the deployment, scaling, and management of containerized applications is called Kubernetes. Kubernetes aided data scientists in their quest for scalable utilization of containerized data science platforms.
Utilizing computing environment resources, such as GPU, efficiently is made possible by Kubernetes. Additionally, Kubernetes offers platforms for data science applications that operate in containers and may be used with many cloud providers without concern for the computing environment.
Data Sets consumption: The key component of AI is data. To execute your algorithms and models, you need large data sets. Whether data sets from a cloud environment are shared or not, if you have access to them, you can store them in public or private cloud environments to work on them quickly.
Software development (Dev) and IT operations are combined in a set of procedures called “DevOps” (Ops). In a conference of the same name, the phrase was first used. Although DevOps may be used in any development environment, it is especially necessary for microservices architecture development.
The application development lifecycle, not the data science life cycle, is addressed by DevOps. Gartner thus created the term “ModelOps”. Before ModelOps, DevOps was extended by MLOps (Machine Learning Operations).
Numerous cloud-native (Kubernetes) settings with on-premises availability of data science platforms and AI API services. NLP, Vision, and Automated Machine Learning are just a few of the complicated, broad data science and machine learning platforms and API services that almost all cloud providers (AWS, Azure, GCP, IBM, Oracle, and so on) now offer in their cloud environment.
Strong analytics companies like IBM, SAS, and RapidMiner provide their data science and machine learning platforms on various cloud service providers. It appears that cloud service providers and analytics companies will eventually enhance these offerings on the cloud platform.
Availability of talent/skills
- Although traditional AI education and courses were offered in universities, particularly as parts of computer science, applied mathematics, and engineering, independent AI engineering bachelor’s and master’s degrees in data science are currently offered at several institutions; furthermore, renowned colleges provide specific data science courses. Coursera and Udemy offer very popular cloud-based deep learning and machine learning
- Platforms where data specialists interact and compete to design and improve AI, ML, NLP, and Deep Learning algorithms, include Kaggle and Crowd ANALYTIX, which operate in a cloud environment. Everyone can use these platforms. There are some data sets available. New skills can be developed thanks to these new platforms.
AI The approaching digital era will unavoidably involve cloud computing. Artificial intelligence can scale, defend, and manage cloud computing relatively easily. Business transitions into the future digital era, where cloud technology would not exist without artificial intelligence.
Additionally, as businesses will have access to enormous amounts of data and processing power on-site, edge computing, which extends cloud capabilities to on-premises with low latency and even offline capabilities, will offer more use cases (such as video analytics). Besides, it is anticipated that quantum computing would boost AI, particularly machine learning.