Domain 3 of 6 · Chapter 2 of 3

Google Cloud AI/ML Solutions

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Included in this chapter:

  • The option ladder: one decision, three rungs
  • Reading the four tradeoffs across the rungs
  • Matching a use case to the right rung

The AI/ML option ladder: pre-trained APIs vs AutoML vs custom models

TradeoffPre-trained APIsAutoMLCustom models (Vertex AI)
Speed to a resultFastest: call the API immediatelyModerate: train on your data firstSlowest: design, train, and tune
Effort requiredLowest: no training, no labellingLow: supply and label your dataHighest: full model development
Required ML expertiseNone: just call the serviceMinimal: no data-science team neededHigh: data scientists required
DifferentiationNone: everyone uses the same modelSome: trained on your own dataHighest: a proprietary model
Your data needed to trainNo: uses Google's training dataYes: your labelled training dataYes: large, high-quality datasets
Best forCommon, general tasksYour own labels, no ML teamModel as competitive advantage

Decision tree

Does a pre-trained API already do the task well? Yes Pre-trained API Fastest, no data, no ML skill No Need the model itself to differentiate, and have an ML team? Yes Custom model (Vertex AI) Most effort + expertise; most differentiation No Can you supply your own labelled training data? Yes AutoML Custom model, minimal ML skill No Pre-trained API No data to train on yet Always pick the lowest rung that meets the need
Cheat sheet

Cheat sheet

  • Google Cloud's AI/ML products form an option ladder, not a flat menu
  • Choose the lowest rung on the ladder that still meets the need
  • Four tradeoffs drive the choice: speed, effort, differentiation, required expertise
  • Differentiation runs opposite to speed, effort, and expertise on the ladder
  • A pre-trained API is Google's model called as-is, with no training
  • Pre-trained APIs offer zero differentiation because everyone shares the model
  • AutoML trains a custom model on your own data with minimal ML expertise
  • The AutoML signal is 'our own data' plus 'limited ML experience'
  • Custom models on Vertex AI are the rung you climb for differentiation
  • Custom models need substantial high-quality data and ML staff; AutoML needs much less
  • Match the use-case signal words to a rung
  • Each rung up the ladder is slower and higher-effort to a working result
  • BigQuery ML is the right call when data is in BigQuery and the team writes SQL

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References

  1. Vertex AI
  2. Cloud AutoML
  3. Cloud Digital Leader Certification Exam Guide