Artificial intelligence (AI) has the power to drive innovation, but many companies still grapple with how to realize the full potential of this technology.
On average, only 54% of AI projects ever make it from pilot to production. One central problem is that most companies have insufficient AI infrastructure. They also face an array of workflow challenges, from finding, ingesting and analyzing high-quality data to scaling AI models into production. A persistent AI and machine learning (ML) skills gap also makes it difficult for companies to accelerate and scale AI operations.
AI will reshape many facets of our everyday lives, but to innovate with this transformative technology, organizations must start with a strong foundation. This is why it’s so critical for them to make AI infrastructure a core part of their AI strategy. However, building AI infrastructure from the ground up is time consuming and resource intensive. Instead, companies need a platform for building, deploying, and scaling ML models faster, so they can accelerate AI-driven innovation and maintain their competitive advantage.
Standing up powerful, flexible AI infrastructure with Vertex AI
Building AI-driven applications and enhancing existing solutions with AI is difficult because creating and managing scalable and flexible AI infrastructure across hardware, software, and open source tools is still complex.
Vertex AI is designed to address this challenge. With this unified data and machine learning platform, companies can access fully managed AI infrastructure on Google Cloud, which supports diverse ML workloads with varying hardware needs across CPUs, GPUs, and TPUs. This optimized managed infrastructure also gives teams access to models and solutions from Google Research, DeepMind, and Google partners, helping practitioners focus on innovating and deploying ML models into production instead of managing clusters and nodes.
Teams can use Vertex AI across many use cases and experience several key benefits. Fully managed ML tools, libraries and workflows enable teams to fast-track the development of custom AI models. Vertex AI allows them to unify their data and AI environment and integrate data from a variety of sources using Google BigQuery, Spark, and Cloud Storage. The platform also provides a range of compute, storage and networking capabilities to meet different performance and budget needs, gives data scientists the flexibility to customize their infrastructure stack, empowers teams without infrastructure expertise to train and serve high-performing, low-latency AI models, and build new-era generative AI applications using text, images, code, videos, and audio.
One set of AI solutions, many potential use cases
Google Cloud customers such as Palo Alto Networks and AI21 Labs are already capitalizing on these powerful capabilities to transform their businesses. Palo Alto Networks, a leading cybersecurity company, has used Vertex AI to build an AI-driven autonomous digital experience management solution that automates complex IT operations for the company and its customers.
Vertex AI has given Palo Alto Networks the high-performing AI infrastructure it needs to run large time series models globally, efficiently train AI models to reduce network data drift, and cost efficiently scale custom AI and ML models — all while using minimal engineering resources.
Sourav Chakraborty, distinguished engineer at Palo Alto Networks, says that Vertex AI “gives us the ability to bring models to production faster and maintain them cleanly over time.”
AI21 Labs, a startup that uses generative AI to revolutionize how people read and write, also has used Google Cloud to address its infrastructure challenges.
Barak Lenz, CTO at AI21 Labs, says the company initially had difficulty finding flexible AI infrastructure that would allow it to use a range of GPUs. AI21 Labs ultimately found a solution by using Google Cloud GPUs to build and serve language models with up to 178 billion parameters. The models power text generation and comprehension features in thousands of live applications. Integrating with Google Cloud’s infrastructure was smooth and reduced time to deployment. AI21 Labs now can use a variety of GPUs in a more cost-effective way to meet changing demands from different users, which allows it to deliver a better user experience while spending as little as possible, Lenz says.
Seize the opportunities of AI
Companies like Palo Alto Networks and AI21 Labs that make AI infrastructure a central part of their AI strategy will drive the most value and innovation from this technology. Building and training AI models is complicated work, but with optimized infrastructure and software out of the box, teams can easily access and harness state-of-the-art, transformative AI.
To learn more, tune into a recent session with Google Cloud, Palo Alto Networks and AI21 Labs at the Google Cloud IT Heroes Summit. This is a follow up to last year’s session with Uber, Cohere, Arbor Bio and Credit Karma.