概览
主要功能
- Vertex AI 用于自定义模型开发
- 预训练 API 用于视觉、语音、语言
- 生成 AI Studio 和模型花园
- 无代码.train 的 AutoML
- MLOps 管道和监控
- 与 BigQuery 和 Cloud Storage 集成
价格
- 模型
- Freemium
- 分类
- 王常服务
- 评分
- 4.4 / 5 (5)
使用场景
在规模上部署自定义 ML 模型
使用 Vertex AI 训练、调优和在管理型基础设施上部署自定义机器学习模型。并且使用 MLOps 管道和生产中生命周期管理
在 APP 中加入视觉、语音和语言功能
使用预训练 API 为图片识别、转录、翻译和文本分析等功能提供所需的功能，不需要从头训练模型
使用 Gemini 打造创造性 AI 体验
利用生成 AI Studio 和模型花园，构建使用 Google 基础模型创建内容生成和话语 AI 应用程序的原型和部署应用程序
在 BigQuery 上运行化 analytics
通过组合 Vertex AI 和 BigQuery, 并且结合 Cloud Storage，让您直接在企业数据上构建预测模型，简化 ML 流程，并且在 Google Cloud 供应链中横向移动
优点 & 缺点
优点
- 访问 Google 的 Gemini 等 基础模型
- 可扩展的管理型基础设施
- 预训练 API 的广泛选择
- 与 Google Cloud 数据工具的紧密集成
- 与 BigQuery 和 Cloud Storage 集成
缺点
- 在规模上价格会变得复杂
- 新人需要更大的学习曲线
- 一些功能需要 GCP 锁定
评测
5 个评分的平均值。
登录以留下评测。
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on autoML for no-code training, and access to Google's foundation models like Gemini caught me off guard. still, I'd recommend giving it a real trial.
Use it every day
Honestly didn't expect to like it this much. Vertex AI for custom model development is exactly what I needed, and scalable managed infrastructure. I do wish some features require GCP lock-in, but I reach for it almost every day now and it just clicks.
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on generative AI Studio and model garden, and broad set of pre-trained APIs caught me off guard. Some features require GCP lock-in is why this isn't a perfect score, still, I'd recommend giving it a real trial.
Does the job
Pretty happy overall. Generative AI Studio and model garden just works and access to Google's foundation models like Gemini. Pricing can get complex at scale can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.
Compared a few options
Evaluated this against two competitors. Where it wins: mLOps pipelines and monitoring and tight integration with Google Cloud data tools. On balance the feature set — especially mLOps pipelines and monitoring — justifies the 5 stars for our use case.
问答
What learning curve should my team expect when starting with Vertex AI and the pre‑trained APIs?
Vertex AI offers both no‑code AutoML for quick model creation and a full SDK for custom development, so beginners can start with AutoML while more advanced users may need time to master pipelines, MLOps tooling, and model tuning. Overall, newcomers should anticipate a steeper learning period compared to purely plug‑and‑play APIs.
Asked by Umar Farooq · Nov 4, 2025
Can I integrate Google Cloud AI with my existing data in BigQuery and Cloud Storage?
Yes. Google Cloud AI natively connects to BigQuery, Cloud Storage, and other GCP services, allowing you to pull data for training, generate embeddings, and run inference directly where the data resides, and to store model outputs back into the same ecosystem for further analysis.
Asked by Kenji Watanabe · Aug 10, 2025
How is pricing structured for Google Cloud AI services and why can it become complex at scale?
Google Cloud AI charges based on usage metrics such as API calls, token counts for generative models, and compute resources for Vertex AI training and deployment. As workloads grow—especially with high‑volume pre‑trained API calls or large custom model training—the combined costs across these dimensions can add up, making the overall bill harder to predict without careful monitoring.
Asked by Fumiko Sato · Aug 6, 2025
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