Use case type
This category provides infrastructure and workflows for training and fine-tuning machine learning models. It helps teams build models more efficiently and manage the compute demands of development.
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13
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9 mo
Adoption over time
By case publish month · completed months only
13 cases documented across 33 months (Nov 23 – Jul 26), peaking at 4 in July 2026.
Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.
Company examples
10 shown from 13 use cases
Ethara AI is an India-based reinforcement learning as a service provider that scaled RL infrastructure on Google Cloud.The company uses Gemini Enterprise Agent Platform, Gemini API, and Google Workspace to standardize governance, integrate reward and judge scoring into training loops, and reduce collaboration overhead.
Futureverse is a New Zealand-based AI and metaverse technology unicorn that uses Alibaba Cloud's PAI Lingjun Intelligent Computing Service as the heterogeneous computing foundation for foundation model training.The company is developing JEN-1, a text-to-music generator that aims to deliver higher fidelity audio, longer and more complex musical compositions, and efficient large language model training.
ZKH Group Limited is an MRO procurement platform that modernized legacy procurement workflows and expanded globally with Alibaba Cloud.The company built its own industry model in 2024 using the open-source Qwen model on Alibaba Cloud and fine-tuned it with e-commerce data to create a specialized expert model for daily MRO scenarios.ZKH deployed multiple AI agents to standardize materials, provide interactive services, compare supplier prices, and recommend products from natural-language requirements.The broader platform architecture also included ECS GPU Instances, ACK, ASM, OSS, PolarDB, MaxCompute, and Cloud Enterprise Network to support AI, storage, data processing, and international connectivity.
Encord provides a data layer for AI development that turns messy multimodal information such as video footage and medical scans into high-quality training data.The company migrated to Google Cloud to build a platform that continuously processes data, identifies critical events, and helps customers curate and annotate training data faster.
Outpost VFX is a media and entertainment company delivering high-end film and episodic content across studios in the UK, Canada, and India.The team adapted its face swap model codebase to support distributed GPU training across multiple GPUs on AWS EC2 P5 instances, using AWS SageMaker AI context and help from the AWS Generative AI Innovation Center.The architecture ran in a segregated secure AWS environment and enabled faster iteration, higher-resolution images, and larger datasets for the face replacement workflow.
Azercell Telecom LLC built an Azerbaijani large language model on Amazon SageMaker AI for telecom use cases and a customer-facing chatbot using a custom tokenizer, continued pre-training, and LoRA fine-tuning.The implementation used distributed training with PyTorch FSDP and Liger Kernel optimizations, plus Amazon SageMaker Unified Studio, Amazon EC2, Amazon S3, and Amazon CloudWatch.
Organizations pursuing AI transformation can face a familiar challenge: how to upskill their workforce at scale in a way that changes how teams build, deploy, and use AI.Atos partnered with AWS to deliver a hands-on, gamified learning experience through the AWS AI League to accelerate applied AI skills across the organization.Atos selected a use case called the Intelligent Insurance Underwriter to fine-tune a large language model capable of analyzing insurance scenarios and providing underwriting guidance.
Hexagon, the global leader in measurement technologies, collaborated with Amazon Web Services to scale AI model production for point-cloud workflows.The company built a managed training environment to pretrain state-of-the-art segmentation models for built-environment and geospatial use cases, with an integrated data pipeline, compute cluster management, and MLOps monitoring stack.
Obungi AI GmbH delivers hands-on workshops and consulting services that guide customers through the full machine learning lifecycle in Microsoft Fabric.The workshop covers data ingestion, feature engineering, AutoML and custom model building, deployment with Fabric MLOps, ad-hoc and batch inferencing in Power BI and automated pipelines, and a Data Agent for on-demand product Q&A.
Microsoft and Yotta Data Services are collaborating to accelerate AI adoption in India using Azure AI and Yotta's sovereign cloud platform Shakti Cloud.The partnership targets faster model training, real-time inferencing, and development of hybrid AI models that prioritize safety and trust.It also aims to support IndiaAI Mission goals and enhance access to cutting-edge AI capabilities for enterprises across sectors.
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