Generative AI is rapidly advancing and being adopted by organizations across industries. However, effectively implementing this powerful technology requires having the right supporting technology infrastructure and strategy in place. This article will explore the key technologies organizations need to leverage the benefits of generative AI.
- Robust connectivity and computing power like high bandwidth, low latency networks, high core count processors, and fast GPUs are essential for running data-intensive generative AI models.
- Data pipelines and lakes allow continuous flow of quality data to train and improve generative AI models over time.
- MLOps platforms automate the machine learning lifecycle to accelerate and govern generative AI development from prototype to production.
- Cybersecurity measures like encryption, access controls, and continuous monitoring ensure safe deployment and use of generative AI systems.
- Hybrid and multi-cloud deployment provides flexibility to host models cost-effectively on different platforms for optimal performance.
- Responsible AI practices around transparency, ethics testing, and consent build public trust and minimize legal and reputation risks from irresponsible use.
Robust connectivity and computing power
The foundation for utilizing generative AI is having robust connectivity and computing power. Generative AI models are data and computation intensive. They require accessing large datasets in the cloud and significant processing capabilities. Organizations need high bandwidth, low latency connectivity to cloud platforms where the AI models are hosted. Wired fiber optic connections are ideal for reliable throughput. Computing infrastructure should include high core count processors, abundant RAM, and fast GPUs for parallel processing. Having scalable connectivity and computing allows organizations to fully exploit large language and multi modal generative AI without infrastructure bottlenecks.
Data pipelines and lakes
Data pipelines and data lakes enable the continuous flow of quality data that generative AI models need for training. The models’ output is only as good as their input data. Data infrastructure must ingest data from diverse sources, process it into usable formats, and make it easily accessible to AI models. Automated pipelines save time and effort in moving data to where it’s needed. Centralized data lakes allow unified access with consistent governance.
With reliable data pipelines and lakes, organizations can efficiently train custom AI models on domain specific company data to produce highly relevant output personalized for their business.
MLOps (Machine Learning Operations) platforms provide the middleware to efficiently orchestrate AI workloads from prototype to production. MLOps automates the machine learning lifecycle of model building, training, evaluation, deployment and monitoring.
MLOps enables organizations to version control AI models, reuse components, track data lineages, set up continuous integration/delivery (CI/CD) pipelines, and manage experiments at scale. This is essential for accelerating and governing generative AI development to quickly deliver business value. With MLOps, organizations can productize generative models and update them through efficient iteration as new requirements and data emerge.
Applying generative AI also requires implementing robust cybersecurity measures around data and models. Generative models can reproduce sensitive customer data implicating privacy laws. Attackers could also misuse models for phishing campaigns or generating misinformation.
Organizations need data encryption, access controls, multi factor authentication, endpoint monitoring, and network intrusion prevention systems for comprehensive security. AI model behavior must be continually monitored for signs of data poisoning or adversarial attacks attempting to corrupt output. Rigorous cybersecurity allows organizations to safely develop, deploy, and monitor generative AI systems integral to the business without comprising data or reputation.
Hybrid and multi-cloud deployment
Most generative AI models require leveraging cloud platforms provisioned with abundant GPUs for cost effective training and inference. Hybrid cloud and multi cloud deployments provide flexibility and mitigate vendor lock-in risks.
Hybrid cloud enables keeping sensitive data and workloads private while tapping into public cloud AI services. Multi cloud across AWS, Microsoft Azure, and Google Cloud allows the use of specialized AI tools on different platforms.
Careful selection of target platforms for specific AI workloads improves performance and keeps expenses optimal. A mix of deployment strategies also maintains business continuity if any one cloud provider experiences outages. With hybrid and multi cloud for AI, organizations gain resilience and portability to use generative models however makes the most economic and strategic sense.
Workflow integration systems
To maximize value from generative AI, its capabilities must integrate with organizational workflows through automation and API access. Users across departments should be able to leverage AI functions within existing business applications.
Integration tools like robotic process automation (RPA), business process management (BPM), and custom interfaces seamlessly incorporate AI by exposing it via APIs. This avoids disruptive rip-and-replace upgrades to leverage generative models. With easy workflow integration, employees can enhance almost any activity, from sales lead enrichment to supply chain optimizations, through readily available AI augmenting their daily efforts.
Responsible AI practices
Getting full benefit from generative AI in a socially conscious manner requires following responsible AI practices around transparency and ethics. Models must avoid perpetuating harmful biases or breaching confidential data use policies.
Organizations should conduct AI impact assessments, document model development processes, perform bias testing, enable external audits, and track data processing consent. Understanding generative model behavior builds trust and accountability.
Adhering to secure and ethical AI systems principles is both the morally right thing to do and minimizes legal, PR and data privacy risks from irresponsible AI use. It demonstrates organizational commitment to unbiased, transparent innovation improving lives winning more public trust to leverage AI advances.
User provisioning and access controls
To govern use of powerful generative AI within acceptable parameters, proper user provisioning and access controls should be established around model access.
Identity and access management (IAM) technology can define user roles and privileges aligned with job duties. AI model self service portals allow access under preset constraints aligning with governance policies preventing excessive usage.
Systematized provisioning enables precise generative AI access tailored to user needs. Combined with monitoring usage patterns, this ensures models remain available for appropriate business applications rather than potential misuse or abuse.
Facilitating technology environment
Bringing all these technologies together optimally is crucial so generative AI capabilities can thrive with the right enabling infrastructure. Organizations should view AI readiness holistically assessing if current environments have the necessary foundations for AI success before adopting.
Upgrading connectivity, modernizing data centers, consolidating siloed stacks into unified hybrid cloud, and enhancing cybersecurity posture establish fertile ground for generative AI to blossom in full capability delivering exponential value at scale. The juices technology ingredients precede the fruits of AI innovation.
Investing first in the underlying technology environment pays dividends when smoothly integrating incredible generative AI into business for drawn out impact. With these technology essentials covered, AI can transform organizations with human machine partnerships to new heights.
Capitalizing on the breakthrough potential of generative AI encompasses deploying a mesh of technologies working in concert to amplify possibilities while controlling risks. Robust data and compute form the bedrock. MLOps introduces devops style efficiencies. Cloud provides flexibility with portability. Cybersecurity keeps information safe. Workflow integration ensures adoption. Responsible AI guidelines retain public trust. And accessible user provisioning allows decentralized innovation. With the right technology foundation fuelling generative AI, savvy organizations in 2024 stand ready to revolutionize every facet of business through creative new applications generating value today while establishing competitive advantage for the emerging AI economy of tomorrow.
The supporting infrastructure unleashing generative AI should be viewed as a stacked technology ecosystem synergetic to human centered design. While individual components each serve purposes, collectively they enable orchestrating generative AI harmoniously across the enterprise for immense good.
What is the most important technology for using generative AI?
The most important technology is robust connectivity and computing power (bandwidth, processors, GPUs). This provides the data throughput and scalable processing capacity needed for large generative AI models.
What technology helps make generative AI usable in business workflows?
Integration tools like RPA, BPM software, and custom interfaces incorporate generative AI functions into existing business applications via APIs. This avoids disruptive platform replacements.
How can organizations use multiple clouds for generative AI?
Hybrid cloud allows keeping sensitive data/workloads on premises while leveraging public cloud AI services. Multi cloud across AWS, Azure and GCP provides flexibility to use specialized tools on different platforms.
What is responsible AI and why does it matter?
Responsible AI means transparently developing and monitoring models to avoid bias, ensure confidentiality, and build public trust. This is crucial for getting full long term value from generative systems.
How does access management enable better generative AI use?
Identity and access management (IAM) allows precise access control to generative models based on user roles and governance policies. This prevents potential misuse or overuse.
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