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enterprises never take advantage of it. Many default to building agents on closed foundation models that can be costly and lack the detailed understanding of your business. As open-weight models become increasingly competitive with frontier models, enterprises have an opportunity to customize AI using their own data and context. Doing so, however, has traditionally required managing complex, expensive GPU infrastructure or relying on managed APIs that offer limited flexibility and control.\u003C/p\u003E\r\n\u003Cp\u003EToday, we are excited to announce the public preview of \u003Ca href=\"http://www.snowflake.com/en/product/features/cortex-training\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECortex Training\u003C/a\u003E, Snowflake’s serverless offering for customizing and deploying open-weight LLMs. With Cortex Training, teams can use familiar training frameworks, or build advanced custom training loops with low-level serverless APIs — \u003Cb\u003Eall\u003C/b\u003E \u003Cb\u003Ewithout managing the complexities of GPU provisioning, scheduling, checkpointing and distributed execution\u003C/b\u003E.\u003C/p\u003E\r\n\u003Cp\u003ECortex Training is built on top of a highly customizable and optimized \u003Ca href=\"https://github.com/Snowflake-AI-Research/Arctic-Platform\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Eopen source backend\u003C/a\u003E to bring the \u003Cb\u003Eflexibility and efficiency\u003C/b\u003E of a complex, self-managed bare-metal training cluster to a simplified serverless experience. Benchmarks show up to 61% lower cost than competing serverless offerings,\u003Csup id=\"footnote-ref-1\"\u003E\u003Ca href=\"#footnote-1\"\u003E[1]\u003C/a\u003E\u003C/sup\u003E so \u003Cb\u003Eteams no longer have to trade simplicity for flexibility or cost efficiency\u003C/b\u003E when customizing and deploying models.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_1":{"id":"image-429a190647","height":"536","src":"/adobe/dynamicmedia/deliver/dm-aid--563d5f22-ad7b-416a-9872-068eec35da25/ml-training-approaches-on-axes-of-flexibility-cost.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Diagram comparing ML training approaches on axes of flexibility/cost efficiency vs infrastructure complexity","isLcpImage":true,"width":"928","title":"Figure 1. Cortex Training delivers cost-efficient, flexible open-weight LLM customization through a simple serverless experience — without the complexity of managing GPU infrastructure.",":type":"snowflake-site/components/image"},"blog_text":{"id":"blog-text-986e7cf8f9","text":"\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003EWith Cortex Training, teams can use Snowflake’s integrated data platform to easily curate training-ready data sets and capture agent traces and logs, creating a feedback loop for model customization and improvement.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_2":{"id":"blog-title-b632c06d36","propertiesId":"why-enterprises-need-post-training","type":"heading2","lines":["Why enterprises need post-training"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_3":{"id":"blog-text-8b1e9aa591","text":"\u003Cp\u003EOpen-weight models off the shelf are general by default but can be highly optimized for each company’s domain, proprietary data, use cases, and budget through post-training. Post-training methods such as SFT and RL are how LLMs learn what good behavior looks like and how to make more accurate, cost-effective decisions using your organization’s specific context.\u003C/p\u003E\u003Cp\u003E\u003Cstrong\u003ESFT teaches models what good behavior looks like.\u003C/strong\u003E Using high-quality examples, enterprises can teach a customer service agent how to diagnose issues, communicate with customers, apply escalation policies and follow proven tool-calling and workflow patterns. Rather than relying on prompting to specify every step, the model learns from successful interactions how the enterprise expects work to be done.\u003C/p\u003E\u003Cp\u003E\u003Cstrong\u003ERL teaches models how to make better decisions to achieve desired outcomes.\u003C/strong\u003E Unlike SFT, which learns from demonstrations, RL optimizes behavior through feedback on measurable outcomes, such as issue resolution, policy compliance, latency and cost. For example, a customer service agent can learn when to invoke a ticketing system, call a billing API or escalate to a human. By learning from feedback across many interaction trajectories, it can discover more effective ways to complete tasks, avoiding unnecessary tool calls and steps while balancing quality, speed and cost.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_4":{"id":"blog-title-a13fc4e47b","propertiesId":"introducing-cortex-training","type":"heading2","lines":["Introducing Cortex Training"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_5":{"id":"blog-text-d3d70a1cfe","text":"\u003Cp\u003ECortex Training is Snowflake’s serverless offering for building and deploying customized open-weight models. AI teams retain fine-grained control over SFT, RL and custom training loops, while Snowflake manages the GPU infrastructure so developers can focus on improving models rather than operating clusters.\u003C/p\u003E\r\n\u003Cp\u003ECortex Training is built on top of a highly customizable and optimized \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://github.com/Snowflake-AI-Research/Arctic-Platform\"\u003Eopen source backend\u003C/a\u003E to bring the flexibility and efficiency of complex, self-managed bare-metal deployments to a simplified serverless experience. It supports a wide range of open-weight model families, supervised fine-tuning (SFT) and reinforcement learning (RL) with \u003Ca href=\"https://www.snowflake.com/en/developers/guides/finetuning-llm-using-snowflake-cortex-ai/\"\u003Efull-parameter or LoRA training\u003C/a\u003E, and orchestration through popular open source frameworks such as SkyRL, Hugging Face TRL and veRL.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_6":{"id":"image-a8cf823edc","height":"1200","src":"/adobe/dynamicmedia/deliver/dm-aid--31fc7316-8ab9-479a-9abc-fe917b2ffc29/architecture-diagram-showing-cortex-training-framework-with.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Architecture diagram showing Cortex Training framework with APIs, backend components, and managed GPU clusters for model training","isLcpImage":false,"width":"1800","title":"Figure 2. Cortex Training supports a wide range of models and training methods — including SFT, RL, DPO and distillation — while integrating with major post-training frameworks and enabling custom loss functions and architectures.",":type":"snowflake-site/components/image"},"blog_component_7":{"id":"blog-text-7524c31131","text":"\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003EThe platform supports the full model lifecycle: Benchmark leading open-weight models, customize them with popular training frameworks or low-level serverless training APIs, evaluate and compare checkpoints, and deploy them through OpenAI-compatible inference endpoints. This creates a continuous flywheel in which production data and feedback drive increasingly capable, specialized models.\u003C/p\u003E\r\n\u003Cp\u003ECortex Training extends Snowflake governance controls across the model lifecycle. Role-based access controls govern training data, model training, deployment and inference, while data, model weights and optimizer state remain within Snowflake’s managed environment.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_quote_1":{"id":"quote-item-e7f42b0c8f","alignment":"left","layout":"card","quoteSourceName":"Sean Bell","quoteSourceTitle":"Research Lead, ResolveAI","showQuoteIcon":true,"quote":"At ResolveAI, we’re improving software reliability by putting AI SRE agents on the critical path and owning the outcome. Building superhuman SREs that autonomously investigate and resolve production issues requires training on long-horizon tasks, with jobs running on hundreds to over a thousand GPUs. Cortex Training’s serverless APIs let us operate at this scale without managing the underlying infrastructure or optimizing clusters. The Snowflake team’s systems expertise also complements our AI research, allowing us to innovate rapidly and iterate toward our model performance goals.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_component_8":{"id":"blog-title-5086334905","propertiesId":"focus-on-models-not-gpu-infrastructure","type":"heading2","lines":["Focus on models, not GPU infrastructure"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_9":{"id":"blog-text-20cb7cf136","text":"\u003Cp\u003ECortex Training provides serverless APIs so teams can focus on training logic instead of on provisioning and operating GPU infrastructure.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_10":{"id":"image-2f52f05d6e","height":"867","src":"/adobe/dynamicmedia/deliver/dm-aid--8d9cadfd-4642-4149-a373-ddb2c8eb0817/traditional-gpu-training-cluster-architecture-with.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Diagram comparing traditional GPU training cluster architecture with serverless training approach using Cortex Training Service","isLcpImage":false,"width":"1999","title":"Figure 3. Use serverless APIs to post-train and deploy on fully managed GPUs without the infrastructure management.",":type":"snowflake-site/components/image"},"blog_component_11":{"id":"blog-text-4e460cca70","text":"\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003EYour training script controls the training loop, while Cortex Training APIs handle GPU-intensive operations such as forward/backward passes, optimizer steps, weight synchronization and generation. You define how the model learns; Snowflake manages the GPUs, distributed execution, model state and checkpointing.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_12":{"id":"blog-title-0fba356c5f","propertiesId":"unlock-the-flywheel-of-model-improvements","type":"heading2","lines":["Unlock the flywheel of model improvements"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_13":{"id":"blog-text-1f778fd3ed","text":"\u003Cp\u003ESnowflake enables a flywheel of model improvement — from capturing production agent traces and logs to curating training data, customizing open-weight models for business-specific needs and deploying them toward inference needs.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_14":{"id":"image-333fcf7a2f","height":"1200","src":"/adobe/dynamicmedia/deliver/dm-aid--d82cc4a9-3501-4d8e-8be9-ef1a72ab20b4/flowchart-diagram-showing-a-6-step-machine-learning.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Flowchart diagram showing a 6-step machine learning workflow from data curation through deployment with production feedback loop","isLcpImage":false,"width":"1800","title":"Figure 4. Unlock the flywheel effect by retraining your models on production data generated from your customized AI applications.",":type":"snowflake-site/components/image"},"blog_component_15":{"id":"blog-title-ddc12ae035","propertiesId":"curate-your-data","type":"heading3","lines":["Curate your data"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_16":{"id":"blog-text-fccecb816c","text":"\u003Cp\u003ESuccessful model customization starts with high-quality training data. Snowflake’s data platform enables teams to clean, filter, enrich and label enterprise data — both structured and unstructured — into training-ready data sets with Cortex AI Functions, SQL and Snowpark. \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-ai-gateway\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECortex AI Gateway\u003C/a\u003E helps close the feedback loop between production agents and model training. Teams can capture agent traces, logs and model interactions to understand agent behavior and identify opportunities for improvement. By combining these signals with application outcomes and human feedback, teams can curate high-value training data sets for SFT, preference optimization and RL — turning real-world agent experience into continuously improving models.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_17":{"id":"blog-title-2d8690bee6","propertiesId":"benchmark-and-evaluate-the-right-open-weight-model","type":"heading3","lines":["Benchmark and evaluate the right open-weight model"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_18":{"id":"blog-text-8274e5402b","text":"\u003Cp\u003EStart by testing open-weight models against your workload before investing in customization. Compare candidates on the dimensions that matter for your application: quality, latency and cost. Cortex Training provides a rich catalog of open-weight models, including families such as Qwen, GLM, Nemotron, Gemma and DeepSeek, with batch and streaming inference to facilitate model benchmarking.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_quote_2":{"id":"quote-item-219c9a9276","alignment":"left","layout":"card","quoteSourceName":"Jeff Ma and Parinthapat Pengpun","quoteSourceTitle":"Researchers, OpenHands","showQuoteIcon":true,"quote":"At OpenHands, we use RL frequently to train models for our agent to handle coding tasks when a user points to files or functions. In the past, we’ve had to manage our own training cluster and were spending days to weeks on cluster management and debugging. With Snowflake Cortex Training, we’ve been able to focus 100% of our time on experiments and training. What’s more, we haven’t had to change our preferred frameworks or compromise on flexibility.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_component_19":{"id":"blog-title-84046e4856","propertiesId":"flexibly-train-and-customize-the-model-your-way","type":"heading3","lines":["Flexibly train and customize the model your way"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_20":{"id":"blog-text-493204f0ad","text":"\u003Cp\u003EOnce you have a strong base model, adapt it using the training strategy that best fits your task. Cortex Training supports state-of-the-art open-weight models — including Qwen, Nemotron, Gemma, DeepSeek and others — across a range of parameter sizes. Teams can use LoRA or full-parameter training for SFT, RL, direct preference optimization, distillation, continued pretraining and mid-training.\u003C/p\u003E\u003Cp\u003ECortex Training integrates with popular post-training frameworks such as Hugging Face TRL, PrimeRL, veRL and SkyRL, so teams that are already using these frameworks can onboard quickly. It also integrates with agent platforms such as \u003Ca href=\"https://www.openhands.dev/blog/post-train-ai-models-openhands-snowflake\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EOpenHands\u003C/a\u003E to train models and entire agent harnesses. Beyond these integrations, the serverless APIs enable agent-driven model customization for enterprise workloads, reducing training complexity and making customization accessible without specialized AI expertise.\u003C/p\u003E\u003Cp\u003EFor greater control, advanced ML teams can use lower-level APIs to define custom loss functions, model architectures and training loops. For example, Cortex Training provides APIs for generating rollouts — model responses or agent interactions — updating model weights and synchronizing those weights across the system. These building blocks enable RL workflows that repeatedly generate behavior, evaluate outcomes and use the results to improve the model over time.\u003C/p\u003E\u003Cp\u003EThis flexibility extends to the underlying infrastructure. Cortex Training is powered by Arctic Platform, an open source backend that gives users full visibility into the code running on the GPUs. Advanced teams can contribute PRs and incorporate emerging techniques and innovations from the broader AI/ML community into their training workloads.\u003C/p\u003E\u003Cp\u003ECheck out our \u003Ca href=\"https://github.com/snowflakedb/cortex-training/tree/main/recipes\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECortex Training examples\u003C/a\u003E for the complete set of recipes, integrations and skills.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_quote_3":{"id":"quote-item-a28a7073a7","alignment":"left","layout":"card","quoteSourceName":"Boyi Liu","quoteSourceTitle":"Senior Research Scientist, Snowflake AI Research","showQuoteIcon":true,"quote":"At Snowflake AI Research modeling team, we build our software-engineering agents, including Arctic-SWE, on prime-rl with Cortex Training as the backend. It lets us freely develop our own algorithm frameworks and recipes on top of serverless GPUs — the training dynamics we need for state-of-the-art SWE performance, without managing the cluster.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_component_21":{"id":"blog-title-0b6f2344b3","propertiesId":"evaluate-and-iterate","type":"heading3","lines":["Evaluate and iterate"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_22":{"id":"blog-text-308a99ef27","text":"\u003Cp\u003ETraining is only valuable if the new model is measurably better. Cortex Training lets teams load trained checkpoints for batch inference or deploy them behind endpoints for evaluation and application testing. You can use Snowflake ML Experiments to track training runs, log custom metrics, observe performance and compare runs across experiments, giving you full control over what data is captured rather than relying on automatic logging. Checkpoints are persisted server-side so teams can resume training or start new experiments from previous checkpoints.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_23":{"id":"blog-title-84dc795624","propertiesId":"deploy-to-agents-and-applications","type":"heading3","lines":["Deploy to agents and applications"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_24":{"id":"blog-text-337ac79c93","text":"\u003Cp\u003EOnce a model meets the required quality and cost targets, teams can deploy it for inference.\u003C/p\u003E\u003Cp\u003ECortex Training supports OpenAI-compatible inference endpoints backed by dedicated GPUs, including streaming inference for interactive applications and agents and batch inference for noninteractive needs.\u003C/p\u003E\u003Cp\u003EThe lifecycle then starts again as production applications generate new trajectories, evaluations and outcomes: benchmark → train → generate rollouts → evaluate → deploy and serve → learn from production → train again. That loop is how enterprise data and application experience become increasingly capable, specialized models.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_25":{"id":"blog-title-571d378f96","propertiesId":"cost-efficiency-through-an-open-foundation","type":"heading2","lines":["Cost efficiency through an open foundation"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_26":{"id":"blog-text-9a25f4c2f4","text":"\u003Cp\u003ECortex Training provides optimized configurations out of the box, while advanced teams can tune them for specific workloads or contribute improvements upstream. Because usage is billed by GPU time, faster training translates directly into lower costs.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-28e4771cee","height":"1458","src":"/adobe/dynamicmedia/deliver/dm-aid--0cb6f31a-9175-488c-844c-d4f9f74b28c2/cortextrainingnew.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Figure 5: LoRA training cost comparison between Cortex Training and other serverless training API offerings.","isLcpImage":false,"width":"2698","title":"Figure 5: LoRA training cost comparison between Cortex Training and other serverless training API offerings.",":type":"snowflake-site/components/image"},"blog_component_28":{"id":"blog-text-c3bfc7a8f1","text":"\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003ECompared to other serverless training APIs, Cortex Training delivers strong cost efficiency for LoRA training. In our benchmarks, Cortex Training is up to 61% lower cost than competing serverless training API products (Tinker and others).\u003Csup\u003E\u003Ca href=\"#footnote-1\"\u003E[1]\u003C/a\u003E\u003C/sup\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_quote_4":{"id":"quote-item-ea1f5475c6","alignment":"left","layout":"card","quoteSourceName":"Ian Fischer","quoteSourceTitle":"Co-CEO, Poetiq","showQuoteIcon":true,"quote":"Poetiq is building the most powerful optimizer in the world using our own approach to recursive self-improvement. We have started to post-train the first LLM in the world that specializes in optimization with Snowflake. Cortex Training gives us the ease of use and flexibility to handle everything we need to do from LoRA, to SFT, to full-parameter post-training with our custom GRPO implementation.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_component_29":{"id":"blog-title-ba02b7a9c9","propertiesId":"get-started-with-llm-post-training","type":"heading2","lines":["Get started with LLM post-training"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_30":{"id":"blog-text-127308cdde","text":"\u003Cul\u003E\r\n\u003Cli\u003ETo help you get started, we’ve put together this easy-to-use \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/developers/guides/own-your-intelligence-cortex-training/\"\u003Equickstart notebook\u003C/a\u003E that operates entirely inside your existing Snowflake account.\u003C/li\u003E\r\n\u003Cli\u003ERead through the \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://github.com/snowflakedb/cortex-training\"\u003Egetting started repository\u003C/a\u003E, or pick from one of these \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://github.com/snowflakedb/cortex-training/tree/main/examples\"\u003Eready-to-go recipes\u003C/a\u003E.\u003C/li\u003E\r\n\u003Cli\u003ETo learn more about Cortex Training, check out \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-training\"\u003Edocs\u003C/a\u003E or visit the \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"http://www.snowflake.com/en/product/features/cortex-training\"\u003Eweb page\u003C/a\u003E.\u003C/li\u003E\r\n\u003Cli\u003EReach out to your account teams for help or to consult with an AI specialist on Snowflake.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Chr\u003E\r\n\r\n\u003Col data-footnotes=\"true\"\u003E\r\n\u003Cli id=\"footnote-1\"\u003E Cost comparison methodology: Pricing for competing serverless API offerings is based on pricing published on Oct. 22, 2026, on their webpages. We ran LoRA SFT training workloads on Cortex Training with the context length specified in Figure 5, and measured the throughput per H200 GPU. We used the throughput (toks/sec) to calculate price per million tokens for Cortex Training using the formula (price_per_second / throughput) x 1M. \u003Ca aria-label=\"Back to footnote [1]\" href=\"#footnote-ref-1\"\u003E↩\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ol\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_component_0","blog_component_1","blog_text","blog_component_2","blog_component_3","blog_component_4","blog_component_5","blog_component_6","blog_component_7","blog_quote_1","blog_component_8","blog_component_9","blog_component_10","blog_component_11","blog_component_12","blog_component_13","blog_component_14","blog_component_15","blog_component_16","blog_component_17","blog_component_18","blog_quote_2","blog_component_19","blog_component_20","blog_quote_3","blog_component_21","blog_component_22","blog_component_23","blog_component_24","blog_component_25","blog_component_26","image","blog_component_28","blog_quote_4","blog_component_29","blog_component_30"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid 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Rajbhandari","isLcpImage":false,"width":"245",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-cab60959fe","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/samyam-rajbhandari/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Samyam Rajbhandari"},"authorTitle":"Principal AI Architect, Snowflake"},{"authorImage":{"id":"image-2e531264e3","height":"400","src":"/adobe/dynamicmedia/deliver/dm-aid--8a49206a-e926-4d58-822f-a951ce70be29/jeffreygeevarghese.jpg?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"400",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-49e843100f","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/jeffrey-geevarghese/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Jeffrey Geevarghese"},"authorTitle":"Director, Engineering"},{"authorImage":{"id":"image-3441c2b691","height":"512","src":"/adobe/dynamicmedia/deliver/dm-aid--a8252970-74b8-459d-8d57-89f09e3a0766/lucy.jpg?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Lucy Zhu","isLcpImage":false,"width":"438",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-ae02ae8eab","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/lucy-zhu/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lucy Zhu"},"authorTitle":"Principal Product Marketing Manager"}],":type":"snowflake-site/components/blog/author-chip"}},":itemsOrder":["author_chip"],"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium"},"container_share_article":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"share_article":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid 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