Generative AI allows you to generate text, images, code, video and more using simple natural language prompts. Google Cloud announced an extensive partner ecosystem to assist customers on their generative AI journey.
Data collection is key to any generative AI solution and may involve techniques such as data partitioning and sharding, or preprocessing of raw data.
1. Generative models
Developers need tools that enable them to quickly build and deploy generative applications within enterprises, quickly and efficiently. Google Cloud offers tools such as Generative AI Studio and Model Garden that make this transformational technology accessible, enabling developers to customize foundation models in order to tailor AI solutions specifically to business requirements; thus speeding software development times while increasing productivity levels and improving customer experiences.
Generative AI Studio and Model Garden offer developers access to curated open source foundation models for text generation, code debugging and image generation. By using these templates quickly build generative AI apps rather than spending weeks or years collecting labelled data and training or tuning models. This frees them up to focus on providing useful AI applications rather than collecting labels data for training/tuning models.
These services not only speed the path to production but also enable businesses to establish trust in their generative AI applications and results more easily – including security, privacy, governance and explainability measures as well as safeguards that protect sensitive or proprietary information such as inferences made from data or models used to produce output.
Estee Lauder Companies and GE Appliances both shared their plans to incorporate Generative AI into their digital transformation strategies at Google Cloud Next. Estee Lauder will use Generative AI to develop copilot experiences for their products that respond quickly to natural language prompts while summarizing complex information in an easily digestible fashion; while GE Appliances plans on using Generative AI to optimize manufacturing practices while simultaneously communicating with consumers while decreasing operating expenses.
2. Data collection
Generate new data by gathering input from users, sensors or devices and turning this input into outputs that can inform new product offerings, enhance customer experiences or resolve technical support issues. Examples of such AI solutions are chatbots/voice assistants/customer service apps/data-driven advertising/search analytics solutions etc.
Generative AI offers enterprises a simplified entryway into AI technologies. Instead of collecting labeled data and training or tuning models, this process becomes significantly simplified so developers can focus on crafting solutions tailored specifically to their needs and objectives. Generative AI Studio and Model Garden further facilitate this flexibility with their host and fine-tuning of Foundational Models ready for deployment – further increasing productivity for organizations looking to incorporate AI technologies.
These capabilities, along with various infrastructure configurations and tools for optimizing models, enable companies to easily scale Generative AI initiatives as their business expands. Businesses can quickly begin using AI to power applications that increase productivity and drive business outcomes more rapidly.
Google Cloud continues to lead in Generative AI technology by offering an end-to-end Machine Learning platform (Vertex AI) for developers and enterprises to train, manage, deploy and monitor machine learning models. It features various compute options and GPU resources ranging from NVIDIA A3 Virtual Machines with A-series H100 GPU support up to Intel Xeon processor-powered instances, giving developers and enterprises access to performance needed for their machine learning models.
Customers and partners including Wayfair, GE Appliances, Deutsche Bank, Bayer, Shopify, Orange Vodafone Estee Lauder Companies and Mayo Clinic have already experienced the positive outcomes from Generative AI technologies. At Next 2023 they shared their stories of success and discussed its effect on their businesses.
3. Preprocessing
Google Cloud offers infrastructure optimized to run cutting-edge generative AI models, and offers tools and platforms for users of different levels of experience with machine learning. In addition, there’s a growing partner ecosystem to help simplify application development, deployment and management – as well as setting standards that provide organizations with trustworthiness, resilience and efficiency while mitigating risk.
Generative AI models may be complex and time-consuming to build, but their rewards can be significant. Businesses using them can quickly develop less toxic household cleaners or novel alloys; make product improvements such as more effective drugs or better medical diagnoses; or generate creative assets like images and videos more quickly than before. They also enhance workers’ ability to draft text or edit images; simplify content classification; generate software code automatically without human interference, or enhance chatbot performance.
Google Cloud offers both first-party foundation models as well as third-party commercial and open source models from third parties on its platform, including its Transformer Architecture which supports LLMs like Meta’s Llama 2 and CodeLlama or Falcon LLM; in Imagen model for image creation it now includes style capabilities so organizations can generate images that adhere to specific brand guidelines.
Wendy’s Corp’s FreshAI, UKG’s Vertex AI for HR and DocuSign Inc’s smart contracts assistant are among some of the enterprise applications using generative AI. Bayer AG is working with Google Cloud to speed drug discovery by taking advantage of their high-performance computing capabilities to run large quantum chemistry calculations on Tensor Processing Units; HCA Healthcare LLC and Google are working together on an initiative using generative AI to help doctors and nurses more efficiently document patient information.
4. Training
Establishing generative AI applications has never been simpler, thanks to foundation models provided by Google Cloud Services. Companies can focus their time and resources on application design and engineering rather than spending hours collecting labeled data or building and tuning models themselves, which reduces time and risk for developers while helping their applications scale faster. Plus, when integrated with other services, companies they get all they need for successful operation of their applications.
Businesses looking to take full advantage of GenAI can deploy their models on Google Cloud’s machine learning platform as a service (ML PaaS) — Vertex AI. This platform enables rapid development and deployment of cutting-edge GenAI models with enterprise-grade security, deployment support, access controls, as well as using a central model repository and data management system for consistency across the organization.
Estee Lauder Companies uses Generative AI effectively through large language models for voice-driven search and translation in its Beauty AI mobile app, improving makeup product creation and marketing processes as a result of Generative AI technology. Meanwhile, telecom firm Orange employs call center agents who transcribe and summarize customer calls in order to speed up customer interactions faster while improving the quality of customer interactions.
Google is expanding its partner ecosystem and accelerating AI adoption in business by offering generative AI capabilities and expanding partner ecosystem. One such partnership with SAP SE enables SAP customers to use Google’s generative AI capability with SAP Datasphere and Vertex AI platforms, allowing customers to incorporate third-party environmental, social and governance datasets into existing SAP systems through this partnership.
5. Deployment
Google Cloud Generative AI Studio makes it easier for organizations to design and deploy software applications that use generative AI technologies by providing comprehensive tools, powerful infrastructure, and an open approach that addresses various industry challenges and use cases.
This solution gives developers the freedom to customize foundation models to suit the unique business requirements, shortening development times and resources while mitigating risks associated with AI applications that do not meet requirements. Fine-tuning model parameters is made easy via an intuitive interface while the platform also includes tools for producing models in production mode.
Google Cloud’s Generative AI deployment solution features sample reference architectures and workflows designed to address many business processes and industry-specific use cases. These frameworks aim to help customers get started faster by eliminating some of the tedious manual coding and configuration tasks required in creating AI-powered software apps from scratch.
Generative AI Studio and Vertex AI platforms not only feature an efficient deployment approach, but they also come equipped with robust data protection features to protect all created applications and underlying data, while supporting multiple compute options and GPU configurations to optimize latency and memory usage.
Companies can apply generative AI to many use cases, including customer service, sales and technical support. Generative AI technology can create sophisticated chatbots and voice assistants with human-like interactions and tailored support tailored specifically to an individual user’s goals and preferences. At Slalom Technologies we are harnessing Generative AI’s power to develop innovative solutions across industries – for instance working alongside Deutsche Bank using Generative AI to automate complex tasks that would otherwise require human effort to accomplish.
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