Google Cloud Vertex AI - Google's Kitchen Sink ML Platform

Vertex AI is Google's 2021 attempt to fix their fragmented ML services mess. Instead of managing separate tools for training, serving, and data labeling, they slammed everything into one platform. Good news: it works. Bad news: your first month bill will make you question your life choices.

The platform replaced Google's old AI Platform, which was a nightmare of disconnected services. Now you get one unified interface that does everything from AutoML to custom training to serving models at scale. Think of it as AWS SageMaker's younger, shinier, more expensive cousin.

What You'll Actually Deal With

AutoML That Works (Sometimes): Google's AutoML is genuinely decent for standard use cases. Upload your data, click some buttons, get a model. It'll handle tabular data, images, video, and text without you needing to understand gradient descent. The catch? It's a black box, so when it fails, good luck figuring out why. Check AutoML limitations before committing.

Custom Training for When AutoML Isn't Enough: When you need actual control, Vertex Workbench gives you Jupyter notebooks with Google's infrastructure behind them. It's basically managed notebooks that scale to TPUs and GPUs. Works great until you hit quota limits or your job fails silently at 90% completion. The troubleshooting docs help sometimes.

Model Registry That Actually Tracks Shit: The Model Registry keeps track of your models, versions, and lineage. No more "where the hell is model_final_final_v3.pkl" moments. It automatically captures metadata and makes handoffs between teams less of a clusterfuck. Integration with MLflow makes migration easier.

Current Model Lineup (September 2025)

As of right now, Vertex AI supports the latest Gemini 2.5 Pro and Flash models, plus Gemini 2.0 Flash for newer use cases. They finally retired the old Gemini 1.5 models (RIP) for new projects as of April 2025.

The Model Garden is where you'll find pre-trained models and foundation models. It's like a marketplace for AI models, which sounds cooler than it actually is. Most of the time you'll just use Gemini variants anyway.

Infrastructure-wise, you get access to TPUs (Google's custom chips) and various GPU types. TPUs are Google's magic chips that are blazing fast when they work. When they don't, you'll spend 3 days reading error messages that might as well be in Klingon like Error compiling HLO. The main thread cannot perform the operation or XLA compilation failed with status INTERNAL: Could not find a tiling that works for this operation. The debugging docs assume you have a PhD in distributed systems and intimate knowledge of XLA compiler internals.

Integration Reality Check

Vertex AI plays nice with other Google services like BigQuery, Cloud Storage, and Cloud Run. This is actually one of its strengths - if you're already in the Google ecosystem, data doesn't need to move around much.

The MLOps pipeline stuff works well for automating training and deployment, though expect a learning curve if you're coming from Jenkins or GitHub Actions. When it works, it's magical. When it breaks, you'll be reading Kubernetes logs trying to figure out why your pipeline died.

Security is enterprise-grade with IAM integration and audit logging. If you're in a regulated industry, Vertex AI checks most compliance boxes. The security best practices guide covers encryption, access control, and data governance. For advanced security scenarios, check the VPC Service Controls documentation. Just don't expect the security team to understand why your ML training job needs to read from 50 different data sources.

AutoML - Good Until It Isn't

Vertex AI AutoML works great for the happy path. Upload clean data, get a model, deploy it, done. The problems start when your data is messy (it always is) or when you need to understand why your model decided that a cat is a dog.

AutoML handles tabular data, images, video, and text. For tabular stuff, it'll automatically try different algorithms and hyperparameters. The 100GB dataset limit sounds generous until you realize that's post-processing - your original data needs to fit in memory first, which often means it doesn't.

Reality check: AutoML works for about 70% of use cases. The other 30% require digging into custom training because AutoML made decisions you can't understand or fix. When AutoML fails, the error messages are gems like:

• INVALID_ARGUMENT: Dataset contains invalid data - no details about which data or why
• FAILED_PRECONDITION: Training could not start - after waiting 45 minutes for it to begin
• Resource exhausted - because your 50MB dataset somehow needs 16GB RAM to process
• Model export failed - after 6 hours of training completed successfully

The known issues page exists because these problems are so common. Pro tip: when AutoML says "Try again" for the third time, start looking at custom training options. Check Stack Overflow for solutions to the cryptic error messages - community answers are better than Google's official troubleshooting.

Custom Training - Where the Real Work Happens

Custom training is where you'll spend most of your time if you're doing anything non-trivial. Vertex AI supports TensorFlow, PyTorch, scikit-learn, and XGBoost in pre-built containers, or you can bring your own Docker container and pray it works.

Distributed Training Pain: Google promises automatic distributed training, but configuring multi-node jobs properly takes expertise. TPUs are blazing fast for certain workloads but debugging TPU code is like debugging with one eye closed. When a job fails 4 hours into a 6-hour training run, you'll question your career choices. The interactive debugging shell helps, but only after you've already wasted time and money.

Hyperparameter Tuning Waste: Vertex AI Vizier runs Bayesian optimization to find optimal hyperparameters. In practice, you'll burn through hundreds of dollars in compute before finding parameters that are maybe 2% better than your initial guess. The service is smart, but hyperparameter tuning is inherently expensive. Check pricing examples before starting large tuning jobs.

Container Hell: Custom containers work great when they work. When they don't, you get cryptic errors like OSError: /lib/x86_64-linux-gnu/libz.so.1: version ZLIB_1.2.9 not found because Vertex AI's base images use different glibc versions than your Docker Desktop.

Common container failures that will ruin your day:

• ImportError: /usr/local/lib/python3.9/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so: undefined symbol: _ZN10tensorflow8OpKernel11TraceStringEPNS_15OpKernelContextEb - CUDA/cuDNN version mismatch
• ModuleNotFoundError: No module named 'google.cloud' - forgot to install the Vertex AI SDK in your container
• Permission denied: '/tmp/model' - container user permissions are fucked, need to add USER root or fix ownership
• CUDA out of memory at training start - your container works locally with CPU but explodes on GPU instances

Pro tip: Docker containers that work on your laptop will mysteriously fail in Vertex AI because of glibc version conflicts, CUDA driver mismatches, or because Google's container runtime hates your Dockerfile. Test everything in Cloud Build first, not your local machine. The container requirements docs are essential reading but won't save you from spending a weekend debugging why pip install torch works locally but times out in Vertex AI.

Model Garden - Marketing vs Reality

The Model Garden is Google's AI model marketplace. It has Gemini variants, some open-source models, and a handful of third-party options. The selection is decent but not as comprehensive as promised.

Current Model Reality: As of September 2025, you get Gemini 2.5 Pro and Flash models, plus the newer Gemini 2.0 Flash. Pricing starts at $0.15 per million input tokens for 2.0 Flash, going up to $1.25-$10+ for Pro models depending on context length.

Fine-tuning Reality: Model customization through LoRA and prompt tuning sounds simple. In practice, getting good results requires domain expertise, quality training data, and multiple iterations. The "100 examples" marketing claim works for toy problems, not production use cases.

Production Deployment - When Reality Hits

Deploying models to production endpoints mostly works, but cost management will keep you awake at night. Google's auto-scaling is aggressive - it'll spin up resources "just in case" and bill you for them.

Serverless Inference Gotchas: Serverless sounds great until you realize cold starts can take 30+ seconds for large models. Your first request after downtime will timeout, guaranteed. Response times "typically range from 100-500ms" assumes your model is small and your data is simple.

Multi-Model Madness: A/B testing with multiple model versions on one endpoint works until you need to debug which version is causing errors. Traffic routing is black-box magic - when it goes wrong, good luck figuring out why 15% of your requests are hitting the wrong model.

MLOps - Promise vs Pain

Vertex Pipelines is built on Kubeflow, which means you're essentially managing Kubernetes workflows. If you love YAML and enjoy reading Kubernetes logs, you'll be at home. If not, prepare for suffering.

Pipeline Failures: Automated retraining sounds amazing until your pipeline fails at 3 AM because your data source changed format. The monitoring alerts work, but debugging why step 15 of 20 failed requires clicking through a web UI that feels like navigating a maze.

Experiment Tracking: ML Metadata captures everything automatically, which is great until you need to find that one experiment from three weeks ago. The search functionality is terrible, and the UI makes you miss spreadsheets.

Monitoring Reality: Data drift detection is useful when it works. False positives are common - your monitoring will alert you that your model is degrading when actually your data pipeline started including weekend data. Expect to tune alerts for weeks before they're useful.

Cost Horror Stories

Here's what nobody tells you: Vertex AI bills can spiral quickly. That $200 training job for a medium-sized model? Add storage costs ($50/month for datasets), network egress ($75 for moving training data), and the 3 failed attempts before it worked ($600 more). Real cost: $925 instead of the planned $200.

Real production examples that hurt:

• Image classification AutoML: Google's calculator said $150. Final bill: $890 after data preprocessing, failed runs, and storage
• Multi-node training job that failed at 90%: $1,200 down the drain with zero usable output - failed jobs still cost full compute time
• Hyperparameter tuning for 3 days: $2,400 to find parameters 2% better than the first guess
• Model serving with auto-scaling enabled: $3,800/month when a Reddit post drove traffic spike, scaled to 50 instances for 2 hours
• Fine-tuning Gemini Pro for a week: $4,200 because they bill for both base model inference AND fine-tuning compute

The pricing calculator lies because it doesn't account for:

• Storage accumulating at $0.023/GB/month (datasets + model artifacts + logs = $200+/month quickly)
• Network egress at $0.12/GB (moving 1TB of training data = $120 you didn't expect)
• Failed job costs (billed for full compute time even when job crashes)
• Auto-scaling overshoot (scales up fast, scales down slow, you pay for the whole time)
• Debugging time (every CUDA out of memory error costs $50+ while you troubleshoot)

Pro tip: Set up billing alerts at 50% and 80% of your budget, not just 100%. Teams regularly get hit with $5,000+ surprise bills when a training pipeline goes haywire overnight. That $300 free credit? Gone in a week if you're actually using the platform for real work.