The Power of Cloud Modernization and AI
July 12, 2024 | Written by Sree Ravela
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The decision to implement AI solutions with systems hosted on-premises, in the Cloud, or hybrid Cloud solutions depends on various factors, such as data sensitivity, compliance, scalability, system performance, cost, resources, expertise, agility, and drive for innovation. As businesses strive to harness the transformational power of AI, they often overlook a critical enabler: Cloud modernization.
Cloud modernization is reengineering traditional IT infrastructure using Cloud-native technologies. This means moving applications and tools from in-house servers to Cloud solutions offered by leading providers like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), or a private Cloud and unleashing the power of Cloud technologies to deliver innovative solutions to solve customers and companies’ high-impact challenges.
Several Fortune 500 organizations have executed AI solutions in the Cloud and been benefiting from this choice for several years. Netflix is a standout example of spearheading AI to recommend content to its viewers. With the modernization of its infrastructure in AWS, Netflix automatically and elastically scales the AI-driven recommendation engine based on user interactions to ensure personalized content delivery at scale. Modernizing the Cloud supports AI, and this synergy has empowered Netflix to quickly innovate through an enhanced user-centric experience.
Harnessing the Cloud for Successful AI Implementations
Let’s explore how Cloud modernization can help enable a successful AI journey, review real-life examples, and consider factors involved in making crucial decisions.
Architectural Assessment and Planning
Perform a deeper architectural assessment of the current system landscape, including applications, data sources, and infrastructure components from the perspective of AI readiness. Develop a roadmap that aligns business and technology activities with the ultimate business goals. Identify and clearly define areas where AI enables outcomes through specific use cases.
Legacy Infrastructure Modernization
Implement Cloud migration strategies tailored to the organization’s needs, including considerations relevant to workload requirements, cost considerations, and scale requirements. The suggested approach includes:
Rehosting applications, aka “lift and shift” (moving the existing applications to a Cloud environment without significant changes to the code)
Refactoring applications by rewriting or significantly modifying the code to better fit a modern Cloud-native architecture
Replatforming applications by enhancing applications without any major modifications so they’re optimally functional with Cloud operations
Rearchitecting by redesigning the entire application to leverage Cloud-native features and services
Encapsulation by wrapping the existing application code with APIs to expose their functionalities as services
Containerization to help package applications and their dependencies into lightweight, portable containers that can run anywhere.
Infrastructure as code (IaC) to manage infrastructure provisioning through code rather than manual procedures
Data Architecture
Thoroughly evaluate how data is structured and tagged appropriately for AI applications. Adjust the architecture around data integration and access, data integrity and consistency, data governance, elasticity, performance optimization, data protection, regulatory compliance, streaming, and cost considerations. Spotify used Cloud-based data lakes on GCP to store and analyze colossal volumes of user-generated data. By modernizing the data architecture, they optimized the…
Computational Resources
Evaluate the essential Cloud computational resources needed for regular and intense workloads, such as data processing and management, model training and deployment, scale and elasticity, experimentation and iterations, and interoperability among different stakeholder systems. Siemens leverages AWS Sagemaker to train and deploy machine learning models. AWS’s powerful computational resources, including EC2 instances with GPUs and FPGAs, support the intensive computational requirements for training complex models on massive datasets.
Security and Compliance
IAM, MFA, DLP, endpoint security, Zero Trust models, and network security controls (Firewall, VPN, IDS, and IPS) are deployed to protect data, including encryption protocols (TLS, AES, and RSA) that hold firm on hiding private and secure data exchange for AI-fueled innovation. Mayo Clinic has embraced Cloud technologies for enhanced security and compliance.
Integration of AI Tools With Existing Systems and Services
Planning and integrating newly deployed AI tools with existing applications is essential to experiencing AI capabilities’ full potential. For example, JP Morgan integrated its classic risk management systems, virtual assistants, and contract analysis utilizing NLP to improve internal and customer operations.
Scalability and Elasticity
Airbnb wanted to address the changing amplitude of workload applicability and sudden bursts of demand in real-time. By working with AWS’s elastic computing, Airbnb hammered out demand patterns that peak and valley, embracing the opportunity to fine-tune their pricing strategy in real-time. This decision upholds their mission to provide optimal experiences for travelers while maximizing revenue.
I recommend carefully evaluating your organization’s needs for Cloud adoption. The next wave of digital transformation will be at the intersection of Cloud Modernization and AI strategies. This means continuously finding innovative ways to solve customers’ problems, reducing time-to-market responses and significantly reducing operating costs.