GenAI Ops
As generative AI workloads go from proof-of-concept to production, Google Cloud clients are witnessing tangible business benefits from their AI investments. Numerous clients have collaborated with Google Cloud Consulting to implement AI in significant and beneficial ways. To help its clinical study teams find crucial information and produce documents more quickly, Bristol Myers Squibb, for instance, developed a new AI-powered interface. Palo Alto Networks, on the other hand, introduced a number of new AI tools that use Gemini to improve user experience in its copilots and increase security practitioners’ productivity.
Implementing these workloads in production calls for extensive knowledge of big language model architectures, fast engineering, evaluation, and generative AI systems design, among other topics. Now, with the introduction of a new service offering called GenAI Ops, Google is bringing Google’s proficiency in these fields to their clients on a large scale. This new offering will assist businesses in developing their generation AI prototypes into production-grade solutions and will be supplied by Google Cloud Consulting or through their extensive partner ecosystem. It will also give support in critical areas such as security, model tuning and feedback, and optimisation.
GenAI Operations: GenAI Constant Adjustment and Input
With the assistance of Google’s GenAI specialists, elevate your generative AI (GenAI) offering to new heights. By including a continual tuning and feedback pipeline into the system design, their team will enhance the output from your model. This can involve creating automated data pipelines, fine-tuning triggers, and putting in place systems for gathering and incorporating downstream feedback. Prototypes, MVPs, and production-ready models are all covered by this offer.
GenAI Ops: Optimising GenAI Models
With the assistance of Google’s GenAI specialists, elevate your generative AI (GenAI) offering to new heights. To position your team for long-term success, Google’s team will optimise your model output or AI infrastructure, implement the optimised service, and finish a knowledge transfer. This deal is valid for prototypes, MVPs, and production-ready models. It can be customised to meet your unique requirements in terms of model selection, timely engineering, agent routing, integrating the newest APIs, enhancing GenAI methods, enhancing system architecture, cutting latency, or cutting expenses.
Google Cloud now provides users with an open and optimised technology stack for developing AI in addition to a wide range of services to assist users at every stage of their AI transformations, from discovery to production, with the introduction of GenAI Ops.
The procedures needed to prepare AI applications for production are walked clients through in the new GenAI Ops services offering. Among them are:
Engineering, designing, and optimising prompts is crucial to ensuring that models can produce high-quality results and gaining the trust of users. With the use of retrieval augmented generation (RAG), chain of thought, and best practices for fast engineering, Google Cloud Consulting may assist clients in developing solutions that enhance the functionality of their existing AI applications and model outputs.
Crucially, distinct models are frequently appropriate for various use scenarios, and each of these models could call for a unique prompting structure. Google’s knowledgeable teams will assist clients in matching the appropriate model to the appropriate use case as well as the appropriate prompting strategy to the appropriate model.
Performance and system assessment: In order to successfully implement AI in the workplace, models and applications must be continuously evaluated and given feedback. This services offering assists clients in developing mechanisms for automated evaluation metrics utilising technologies such as AutoSxS and GenAI Eval, human evaluation, as well as hybrid techniques, and in designing and implementing an assessment framework customised for their applications.
Model optimisation and ongoing tuning: Gen AI applications and models still need ongoing tweaking and optimisation even when a framework for performance and system evaluation is established. Gen AI Ops offers managed services and solutions for model tuning and optimisation based on benchmarking and user input. To ensure that applications execute as efficiently as possible, this entails enhancing system design and model selection, cutting costs and latency, and utilising the most recent APIs and tools available to orchestrate and create AI agents utilising LangChain or do-it-yourself orchestrators.
Monitoring and observability: Ensuring that AI applications are ready for production requires having a strong monitoring system in place. In order to continuously monitor the performance and operations of their generation AI applications on a wide range of criteria, such as model accuracy and hallucinations, latency, throughput, hardware utilisation, model drift, traffic, and costs, Google Cloud Consulting may assist customers in building observability solutions.
Testing and business integration: The performance and integration of a customer’s applications and models with their business processes in real-world settings is crucial. Customers can get assistance from Google Cloud Consulting with the meticulous planning needed to accomplish this, such as creating a safe and scalable environment on Google Cloud, creating APIs to effectively manage interactions with different models, and putting their models through rigorous unit, integration, and load testing to assess performance under various scenarios.
Educate and empower client teams
Customers that want to see success with their cloud deployments must prioritise training and team enablement in addition to the technical and business planning procedures needed to put AI applications into production. Google Cloud provides a variety of trainings, practical labs, bootcamps, and coursework through the Google Cloud Skills Boost Platform to help teams become more proficient in generative AI and ensuring that client teams are able to create, implement, utilise, and oversee innovative AI applications.
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