Google Cloud AIGoogleova platforma v oblaku za gradnjo, uvajanje in skaliranje AI ter ML aplikacij.
Pregled
Ključne funkcije
- Vertex AI za razvoj prilagojenih modelov
- Predpripravljeni API-ji za vid, govor in jezik
- Generativno AI Studio in modelov vrt
- AutoML za usposabljanje brez kodiranja
- MLOps cevovodi in nadzor
- Integracija z BigQuery in Cloud Storage
Cene
- Model
- Freemium
- Kategorija
- Agnosti AI
- Ocena
- 4.4 / 5 (5)
Primeri uporabe
Uvajanje prilagojenih ML modelov v obsegu
Uporabite Vertex AI za treniranje, optimizacijo in uvajanje prilagojenih strojno-učenja modelov na upravljani infrastrukturi, s cevovodi MLOps za spremljanje in upravljanje življenjskega cikla v produkciji.
Dodajte vizijo, govor in jezik aplikacijam
Integrirajte Googleove predtrenirane API-je za podporo funkcijam, kot so prepoznavanje slik, prepis, prevajanje in analiza besedila, brez gradnje modelov od začetka.
Ustvarjajte generativne AI izkušnje z Gemini
Izkoristite Generative AI Studio in vrt modelov za prototipiranje in uvajanje aplikacij z uporabo Googleovih osnovnih modelov za ustvarjanje vsebin in klepet.
Operativizirajte analitiko na BigQuery podatkih
Združite Vertex AI z BigQuery in Cloud Storage, da neposredno zgradite napovedne modele na podjetniških podatkih, kar poenostavi tokove ML po ekosistemu Google Cloud.
Prednosti in slabosti
Prednosti
- Dostop do Googleovih osnovnih modelov, kot je Gemini
- Razširljiva upravljana infrastruktura
- Obsežen nabor predtreniranih API-jev
- Tesna integracija z Google Cloud orodji za podatke
Slabosti
- Cenovna struktura lahko postane zapletena pri večjih obsegih
- Strmejša učenja za nove uporabnike
- Nekatere funkcije zahtevajo zaklepanje na GCP
Ocene
Povprečje iz 5 ocen.
Prijavi se za oddajo ocene.
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.
Vprašanja
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
Postavi vprašanje
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