A world of information: Google Cloud Next 23

A day after OpenAI launched the enterprise version of its ChatGPT model, Google Cloud Next 2023 kicked off, where the company's most recent product launches were shown and exemplified the existing race to occupy the most dominant position in the field. of AI.

At Plain Concepts we did not want to miss the event that, after three years, returned to its in-person format in the city of San Francisco. Below we summarize some of the most relevant news.

AI platforms and tools

Duet AI

Duet AI, Google Cloud's innovative artificial intelligence platform designed to revolutionize the way users work with the cloud, aims to improve productivity, provide competitive advantages, and increase operational efficiency. Through its expansion, Duet AI has been integrated into a wide variety of Google Cloud services and applications.

One of the key areas of focus for Duet AI is application development. The platform offers expert assistance across the entire software development lifecycle, including code generation, source citation, testing, and API design. Developers can use natural language to understand and improve code, as well as to generate unit tests. This support is available across multiple development environments, helping you maintain workflow and minimize disruption.

Duet AI also streamlines application modernization by assisting in code refactoring. For example, it can accelerate the migration of legacy applications to Google Cloud, simplifying tasks such as converting code from one language to another. This feature is especially valuable for companies that want to update their technology systems more efficiently.

The platform integrates with API management, allowing you to design and publish APIs using natural language requests. This simplifies the orchestration of communications between services and makes it easier to integrate applications into an enterprise environment.

Duet AI is also used to simplify the operation and management of infrastructure and applications. It helps automate deployments, configure applications correctly, and resolve issues efficiently. For example, in Cloud Monitoring, you can translate natural language requests into PromQL queries to analyze time series metrics, making it easier to identify and resolve issues.

In the realm of data analysis, Duet AI is a valuable tool for analysts. In BigQuery, it provides context-sensitive assistance for writing SQL and Python, allowing for faster, more effective analysis. It can generate complete functions, suggest code completions, and provide detailed explanations of SQL queries, making it easy to understand data and its patterns. Duet AI also integrates with Vertex AI, allowing for further optimization of data queries and text analysis. This facilitates semantic search and recommendation queries based on BigQuery data.

Duet AI is also being introduced in Looker to help business users analyze data more efficiently. It offers conversational data analysis, automatic generation of presentations and report-based text summaries, enabling faster understanding of data and creation of LookML models.

Finally, Duet AI plays an important role in cybersecurity by quickly summarizing and classifying threat information, reducing the workload of security professionals, and improving incident detection and response. It integrates with security products such as Chronicle Security Operations, Mandiant Threat Intelligence and Security Command Center.

In short, Duet AI is an artificial intelligence platform that has a broad impact on application development, infrastructure operation, data analysis, and cybersecurity. Its ability to understand natural language and provide contextual assistance promises to improve efficiency and productivity in a wide variety of areas within Google Cloud.

Vertex AI

Vertex AI, the cloud-based machine learning platform developed by Google Cloud, offers a complete workflow to create, train, and deploy machine learning models. It provides support for various types of machine learning tasks, offers tools for data processing and analysis, and includes pre-trained models for common use cases. Vertex AI will make infrastructure management easier, allowing developers, data scientists and researchers to focus on their machine learning tasks. With Vertex AI, users can train and deploy models on Google C infrastructure

loud, which includes AI Platform, Kubernetes and AutoML.

At Plain Concepts we did not want to miss the event that, after three years, returned to its in-person format in the city of San Francisco. Below we summarize some of the most relevant news.

AI platforms and tools

Duet AI

Vertex AI

Vertex Platform

Google Cloud Next has announced improvements to Vertex AI capabilities, including new models in Model Garden, updates to own models, and tools to customize and improve models. New models are highlighted, such as Llama 2 and Claude 2, as well as improvements to models such as PaLM 2, Codey and Imagen.

Additionally, digital watermark functionality for Image is introduced. Vertex AI Extensions have been introduced, allowing the connection of models to APIs for real-time data and real-world actions. All designed to make it easy to experiment and build apps with base models, personalize with business data, and deploy into apps with built-in privacy, security, and responsible AI features. These updates seek to serve both developers and data scientists, regardless of their level of AI experience, and accelerate the adoption of generative AI in companies across various sectors.

Vertex AI Search and Conversation

Vertex AI Search and Conversation from Google Cloud arrive to boost the creation of generative search and chat applications. These products allow developers with little AI experience to build search engines and chatbots that can interact with customers and answer questions effectively. In addition to general availability, features such as searching for multiple shifts and extensions have been added to take actions in real time.

Vertex AI Search enables high-quality multimodal search, and Vertex AI Conversation makes it easy to create natural-sounding chatbots and voicebots. These tools are critical to accelerating the adoption of generative AI and improving the user experience in a variety of enterprise applications.

Vertex Colab

Three years ago, Google launched Vertex AI with the goal of providing the best AI/ML platform to accelerate AI workloads. Vertex AI has since expanded its capabilities, including support for generative AI and developer-friendly products for common generative AI use cases. Also added are over 100 large models from Google, open source contributors and third parties. Despite this expansion, the focus on data science and machine learning engineering remains.

Colab Enterprise has been released in public preview, combining the ease of use of Google's Colab notebooks with enterprise security and compliance capabilities. Ray support in Vertex AI has also been announced to efficiently scale AI workloads. Additionally, MLOps capabilities for generative AI are being advanced with model tuning, model evaluation, and a new version of the Vertex AI Feature Store with support for embedding.

Colab Enterprise enables data scientists to collaborate and accelerate AI workflows, with access to Vertex AI capabilities, BigQuery integration, and code generation. Ray on Vertex AI offers efficiency and scalability, and is ideal for training generative AI models. Additionally, features such as model evaluation and embedding support have been announced in the Vertex AI Feature Store to improve the management of generative AI models in production.

These new features and products are designed to help organizations advance their AI practice, especially in the context of generative AI, and focus on collaboration, scalability, and efficient model management.

Vertex AI with Colab Enterprise and MLOps for generative AI

As we said, there is a significant advance in MLOps for generative AI with model adjustments and evaluation, and a new version of the Vertex AI Feature Store with support for embeddings. With these capabilities, customers can leverage the features they need across the entire AI/ML workflow, from prototyping and experimentation to deploying and managing models in production.

Regarding MLOps, key areas are highlighted such as managing AI infrastructure, customizing with new techniques, managing new types of artifacts, monitoring generated output, and connecting to business data. A new MLOps Framework for predictive and generative AI is presented to address these challenges.

Security

At this Google Cloud Next event, another of the major scenarios that worries all types of companies could not be left behind: security. Their focus on security and how they are addressing the most pressing challenges in the information technology space were detailed. Google Cloud is using artificial intelligence (AI), specifically Duet AI, to strengthen its security solutions and protect against

growing cyber threats.

The approach is comprehensive, ranging from managing and controlling security in AI workloads to incorporating AI into your security products to make them more effective. Additionally, they are providing tools and platforms, such as the Google Cloud Security AI Workbench, that allow their customers to leverage AI to improve security in their own applications and operations.

Duet AI on Mandiant Threat Intelligence

It helps identify tactics, techniques and procedures (TTPs) used by threat actors against organizations, summarizing Google's threat intelligence in an understandable way. Provides insights into the latest threats and how to make threat intelligence actionable across your organization.

Duet AI at Chronicle Security Operations

Simplifies the detection, investigation and response to cyber threats. Provides clear case summaries, context and guidance on important threats, as well as recommendations on how to respond. It also allows searches in natural language to speed up obtaining results.

Duet AI in Security Command Center

Facilitates analysis of security findings and potential attack paths with near-instant analysis of security conclusions. This simplifies complex issues so that even non-specialists can defend their organizations.

Google Cloud

Google Cloud is also introducing additional capabilities to improve cybersecurity in Google Cloud environments, such as agentless vulnerability scanning, Cloud Firewall Plus with next-generation firewall capabilities, and Network Service Integration Manager.

Data security

Private preview of Confidential Computing on 4th Gen Intel

Cloud infrastructure modernization

Google finally recognized that we are at an inflection moment in computing, where demands for workloads like generative AI and large language models (LLMs) are growing exponentially, and traditional infrastructure is no longer sufficient. And under that prism the following news fell:

AI Optimized Infrastructure

Google Cloud has been leading the way in AI for two decades and creating AI-optimized infrastructure solutions. Complete solutions for AI are offered, from computing infrastructure to software and services to train, tune and serve models on a global scale.

New Infrastructure Improvements:

Cloud TPU v5e: Google announced Cloud TPU v5e, which is highly cost-efficient and versatile for large-scale training and inference. Delivers up to 2x higher training performance per dollar and up to 2.5x higher inference performance per dollar for LLMs and Gen AI models compared to TPU v4. It is cheaper than its predecessor, allowing more organizations to train and deploy larger, more complex AI models. It offers great flexibility with support for eight different virtual machine configurations.

A3 VMs: These virtual machines based on NVIDIA H100 GPUs will be available soon. They are ideal for training and serving demanding AI workloads. They offer three times the performance and ten times the network bandwidth compared to the previous generation. A3 VMs operate at large scale, allowing users to scale models to tens of thousands of NVIDIA H100 GPUs.

TPU operability

Google Cloud is making it easier to operate TPUs with the general availability of Cloud TPUs on Google Kubernetes Engine (GKE). Customers can improve AI development productivity by using GKE to manage the orchestration of large-scale AI workloads on Cloud TPU v5e.

Expanded Support for AI Frameworks

Google Cloud offers support for several AI frameworks such as JAX, PyTorch, and TensorFlow, in addition to popular open source tools. They also announced strengthening support for PyTorch with the upcoming PyTorch/XLA 2.1 release.

Training Scalability

“Multislice” technology introduced in preview, allowing AI models to easily scale beyond the limits of physical TPU pods.

In short, Google Cloud is providing an AI-optimized infrastructure that is more cost-efficient and scalable, enabling organizations to address the increasing demands of generative AI and LLMs, thereby accelerating progress in the field of AI and deep learning.

Dockers infrastructure

Advances in container infrastructure and artificial intelligence (AI) announced with a focus on G

KE Enterprise, TPU on GKE and Duet AI on GKE and Cloud Run.

GKE Enterprise

Google Cloud introduced GKE Enterprise, an intuitive, integrated container platform that combines the best of GKE and Anthos. This edition includes a new feature called fleets, which allows platform engineers to group similar workloads into dedicated clusters, apply custom configurations and group-specific policies, isolate sensitive workloads, and delegate cluster management to other teams. . GKE Enterprise also offers managed security features, such as advanced workload vulnerability insights, governance and policy controls, and a managed service mesh service. Additionally, it supports hybrid and multi-cloud environments to run container workloads on GKE, other public clouds, or on-premises with Google Distributed Cloud.

TPU on GKE

Google introduced Cloud TPU v5e, a more cost-efficient and scalable AI accelerator that can scale to tens of thousands of chips. Delivers up to 2x training performance and up to 2.5x inference performance per dollar compared to Cloud TPU v4. It allows you to take advantage of features such as auto-scaling and workload orchestration. Also announced was support for A3 VM with NVIDIA H100 GPU.

Duet AI on GKE and Cloud Run

Duet AI, Google's AI contributor, is now available on GKE and Cloud Run. Helps platform teams reduce manual and repetitive work when running containers on Google Cloud.

Overall, these announcements showcase Google Cloud's commitment to providing cutting-edge container infrastructure and AI capabilities that will help businesses increase their productivity, efficiency, and scalability. Customers, like Equifax, have already experienced significant improvements in security and efficiency with GKE Enterprise, allowing them to manage hundreds of clusters efficiently. Additionally, advances in TPU and Duet AI promise to accelerate the adoption of AI in various enterprise applications.