In May 2025, Google Cloud expanded its sovereign cloud offerings to address increasing demands for data sovereignty and operational autonomy. It launched Google Cloud Air-Gapped, a fully isolated environment designed for sectors with stringent data security requirements, such as defense and intelligence. This solution operates without external network connectivity and is authorized to handle US government Top Secret data. Google Cloud Dedicated, developed in partnership with Thales, a French leader in cybersecurity, offers region-specific services operated by local partners to meet national compliance standards, such as France’s SecNumCloud. The Google Cloud Data Boundary service expansion now offers customers granular control over data residency and access, complemented by the User Data Shield, which incorporates Mandiant’s security assessments to validate application security postures. While these initiatives demonstrate Google Cloud’s commitment to offering flexible, secure, and compliant cloud solutions, challenges remain. These challenges include the limited geographic availability of certain services and complexities in integrating sovereign solutions with existing multi-cloud architectures. Enterprises must carefully assess these factors when considering Google Cloud’s offerings for their sovereignty objectives.

With increasing data volumes, enterprises are readily adopting Artificial Intelligence (AI) and Machine Learning (ML) capabilities to gain business insights and make decisions. However, they face several challenges in deploying ML models to production. As a result, enterprises are leveraging Machine Learning Operations (MLOps) to improve the quality of ML models’ results, achieve business-oriented outcomes, and enhance stakeholder experience. Today, the MLOps market is rapidly evolving in terms of product features, architecture, training and support, deployment options, partner ecosystem, and commercial models. Enterprises looking to adopt MLOps solutions and improve their AI/ML transformation journeys must select the best-fit technology provider for their needs. This compendium provides detailed profiles of 18 technology providers featured on Everest Group’s MLOps Products PEAK Matrix® 2022. Each profile provides a comprehensive picture of the provider’s size and scope of business, product capabilities, partnerships, domain investments, and case studies. Scope: All industries and geographies The assessment is based on Everest Group’s annual RFI process for the calendar year 2021, interactions with leading MLOps technology providers, client reference checks, and an ongoing analysis of the MLOps products landscape Contents: In this report, we analyze the MLOps technology provider landscape and include: Technology providers’ leadership, presence across geographies and industries, and global revenue estimates MLOps’ product offerings, along with key partnerships across the ML life cycle Technology provider investments across talent, infrastructure (centers of excellence / labs), acquisitions, research, academic partnerships, and solutions Recent case studies, with detailed descriptions of the solutions provided Everest Group’s remarks on the strengths and limitations of each technology provider Membership(s) Artificial Intelligence (AI) Sourcing and Vendor Management

As data volumes increase exponentially, enterprises are adopting AI and ML capabilities to gain business insights and make decisions. However, enterprises face several challenges in deploying ML models to production. As a result, enterprises are leveraging Machine Learning Operations (MLOps) for their deployment, monitoring, and collaboration needs to improve the quality and relevance of ML model results, achieve business-oriented outcomes, and enhance stakeholder experience. MLOps is a growing market, rapidly evolving in terms of product features, architecture, training and support, deployment options, partner ecosystem, and commercial models. Technology providers can help enterprises succeed in their AI/ML transformation journeys by implementing MLOps across the enterprise. In this research, we present detailed profiles and assessments of 18 technology providers featured on Everest Group’s MLOps Products PEAK Matrix® 2022. Each profile provides a comprehensive picture of the technology provider’s size and scope of business, product capabilities, partnerships, domain investments, and case studies. Scope The assessment is based on Everest Group’s annual RFI process for the calendar year 2022, interactions with leading MLOps providers, client reference checks, and an ongoing analysis of the MLOps products landscape All industries and geographies Contents This report features: Everest Group’s PEAK Matrix® evaluation of MLOps technology providers and their categorization into Leaders, Major Contenders, and Aspirants An overview of MLOps and key challenges in scaling AI Key ML platform technology trends A detailed assessment of the strengths and limitations of 18 MLOps technology providers in terms of their market impact and vision & capability Membership(s) Data & Analytics Sourcing and Vendor Management

With data volumes rising exponentially over the past decade, the need for enterprises to gain data-backed business insights and make data-driven decisions has also increased. While Business Intelligence (BI) tools have helped analyze historical data and improved reporting within organizations, Artificial Intelligence (AI) and Machine Learning (ML) have driven efficiencies across existing processes and transformed enterprises by providing them with a data-backed competitive edge. However, embedding AI and ML into an organization comes with its challenges. Some of these challenges are developmental, and next-generation low-code/no-code platforms or data scientist teams that develop specific use cases can help address them. Other challenges are operational, and enterprises need to deal with them in a structured manner and ease operationalization across multiple IT systems to make it easier for individual teams to use ML models. This research focuses on the life-cycle management of ML initiatives and how AI technology vendors can help enterprises adopt a structured approach to scaling ML across their organizations by using Machine Learning Operations (MLOps). Scope All industries and geographies Contents In this report, we: Explain various ML applications Examine ML tools and their advantages Identify the key capabilities required to succeed in adopting MLOps Determine enterprise considerations when initiating ML journeys Membership(s) Data & Analytics Sourcing and Vendor Management

Recently, AI has become the bedrock of business transformation for enterprises. The technology is increasingly being seen as a business enabler and pertinent investment in helping firms maneuver and reverse the COVID-19 pandemic’s impact. However, challenges such as the lack of skilled AI talent, increasing time and effort in scaling AI implementation, and rising privacy concerns and regulatory impositions, are impeding organizations’ AI vision. Machine Learning Operations (MLOps), a confluence of machine learning and IT operations based on the concept of DevOps, is emerging as a panacea for enterprises in this scenario. MLOps, a set of practices aimed at streamlining ML life-cycle management, aims to enhance collaboration among data scientists and operations teams, thereby accelerating the scaling of AI. To address enterprise concerns around MLOps, multiple tools and platforms have also emerged across data management, modeling, and model deployment and monitoring. As enterprises embark on their MLOps journeys, it will be imperative for them to assess their existing maturity levels, develop hybrid teams, determine the KPIs required to assess the model, and ensure governance and compliance with industry regulations. Scope All industries and geographies Contents: This report studies: Challenges faced by enterprises in scaling their AI implementations Introduction to Machine Learning Operations (MLOps) Key benefits of MLOps The MLOps ecosystem, including tools and platforms across the value chain Key imperatives for enterprises in their MLOps journeys Membership(s) Digital Services Sourcing and Vendor Management