Google NEXT 2018: Google Cloud Platform and the Pythian Partnership

Day one of the Google Cloud Next 2018 conference at San Francisco's Moscone Center made Google's drive to win enterprise customers very clear. At this conference, Google Cloud is sending a strong message to the technology industry that it is ready for the enterprise and is gaining momentum in bringing large companies to the cloud. One example is Unity, the game engine company, which announced in June 2018 that it would move its own infrastructure to Google Cloud. Gartner has also named Google a Leader in its 2018 Magic Quadrant for Cloud Infrastructure as a Service (IaaS). In 2017, Gartner placed Google in the Visionaries quadrant.

Diane Greene, then CEO of Google Cloud, set out the initiatives and principles behind Google's cloud vision and the growth it has seen over the past year. The first initiative is Google's commitment to open source and its close work with the open-source community. Two key examples are TensorFlow for machine learning and Kubernetes for container orchestration. Both have grown into widely adopted technologies with strong community best practices. The second initiative is Google's push for security and artificial intelligence (AI) in the cloud. Greene called security the number one worry for customers and AI the number one opportunity.

Security is built into every level of Google's cloud infrastructure and products. For Google, security starts with Titan, a custom security chip in its servers. The operating system, the file system and every application depend on the hardware, so protection has to begin at the hardware level. Titan acts as a hardware root of trust. It makes sure each machine boots from a known good state using verified code. This helps protect machines against firmware attacks such as rootkits. This investment in hardware security is the foundation of Google Cloud's products and infrastructure.

Google is making big bets in AI, and AI capabilities are a big part of its cloud products. They include Cloud Datalab, a cloud-based notebook for machine learning modeling and experimentation, and Cloud Machine Learning Engine (ML Engine) for large-scale model training and deployment. Google Cloud also offers a range of APIs for AI tasks such as vision, natural language processing and translation, and Dialogflow for building conversational agents. At this year's conference, Google announced Cloud AutoML products that help customers train high-quality custom machine learning models on their own data. AutoML Vision moved to public beta, and Google introduced AutoML Natural Language and AutoML Translation in beta.

Several of these products have changed since 2018. Google deprecated Cloud Datalab, archived its code repository in September 2022 and recommends Vertex AI Workbench in its place. Cloud ML Engine became AI Platform. The AI Platform Training and Prediction services shut down on April 7, 2025, and their features live on in Vertex AI. The legacy AutoML Vision and AutoML Natural Language products shut down in 2024 after their features moved into Vertex AI. In April 2026, Google made Vertex AI part of Gemini Enterprise Agent Platform, so Vertex AI Workbench is now Agent Platform Workbench.

The Pythian Partnership

Pythian is a Google Cloud Premier Partner. Pythian works closely with Google to bring enterprise data workloads and products to the cloud and to help companies shape their cloud strategy and build their cloud skills. Pythian's Kick AaaS (Kick Analytics as a Service) gathers data from many sources and formats into the cloud and adds analytics, machine learning and visualization on top.

Google Cloud's commitment to security fits well with Pythian's own values and its "Love Your Data" motto. Security is the main idea behind Pythian's Tehama, a platform for delivering IT services securely from anywhere in the world. Tehama became a separate company in 2019. Pythian is also well aligned with Google Cloud's AI strategy. Its big data and enterprise data science teams have deep experience in AI, advanced analytics, machine learning and deep learning. These teams help clients improve their decisions and corporate strategy with insights drawn from their data.