ML Lifecycle Stages — The Cycle That Never Stops

contact@corebaseit.com (Vincent Bevia) — Fri, 20 Mar 2026 00:00:00 +0000

ML Lifecycle Stages — The Cycle That Never Stops

Part 4 of 4 in the Generative AI Foundations series

We’ve covered the hierarchy, the landscape, and the data. Now let’s close the loop with the ML lifecycle itself — because building a model is not a one-time event. It’s a cycle. And understanding that cycle is critical, because it’s not linear — it’s iterative.

Models degrade. Data drifts. Requirements change. The cycle runs continuously, and each stage feeds back into the others. If you treat model deployment as the finish line, you’ve already lost.

Here’s how it breaks down, with the corresponding Google Cloud tooling at each step.

1. Data Ingestion and Preparation

The process of collecting, cleaning, and transforming raw data into a usable format for analysis or model training. This is where most of the unglamorous but essential work happens — data engineers will tell you that 80% of any ML project is spent here, and they’re not exaggerating.

This stage is where data quality matters most. Every characteristic we discussed in the previous post — completeness, consistency, relevance, availability, cost, format — comes into play right here. Get this stage wrong, and everything downstream inherits the debt.

Google Cloud Tools: BigQuery for data warehousing, Dataflow for data processing pipelines, and Cloud Storage for raw data storage.

2. Model Training

The process of creating your ML model using data. The model learns patterns and relationships from the prepared dataset. This is the compute-intensive stage where your infrastructure investment pays off — or doesn’t.

Training is where the infrastructure layer from our landscape discussion becomes tangible. You need GPUs, TPUs, or both. You need enough compute to iterate quickly, because model training is inherently experimental — you won’t get the architecture, hyperparameters, or data splits right on the first try.

Google Cloud Tools: Vertex AI for managed training, AutoML for no-code model training, and TPUs/GPUs for accelerated computation.

3. Model Deployment

Making a trained model available for use in production environments where it can serve predictions. This is the bridge between “it works in a notebook” and “it works at scale for real users.”

Deployment is where latency, throughput, and reliability become the primary concerns. A model that takes 30 seconds to return a prediction might be fine for batch processing, but it’s useless for a real-time customer-facing application. The deployment architecture has to match the serving requirements — and those requirements are almost always more demanding than what you tested in development.

Google Cloud Tools: Vertex AI Prediction for serving endpoints and Cloud Run for containerised model serving.

4. Model Management

Managing and maintaining your models over time, including versioning, monitoring performance, detecting drift, and retraining. This is the stage most teams underestimate.

A model that was 95% accurate at launch can degrade to 70% within months if nobody’s watching the metrics. The world changes. Customer behaviour shifts. New data patterns emerge that the model has never seen. Continuous monitoring and retraining pipelines are not optional — they’re operational necessities.

This is also where scaffolding proves its value. The guardrails, logging, and observability infrastructure you built during development become your early warning system in production. Without them, you’re flying blind.

Google Cloud Tools: Vertex AI Model Registry, Vertex AI Model Monitoring, Vertex AI Feature Store, and Vertex AI Pipelines.

The Cycle Continues

The arrow from Model Management loops back to Data Ingestion. That’s not a diagram convenience — it’s the reality of production ML. Monitoring reveals drift, drift triggers retraining, retraining requires fresh data, fresh data requires ingestion and preparation, and the cycle begins again.

The teams that succeed with ML in production are the ones that design for this cycle from day one. They don’t treat it as four sequential steps; they treat it as a continuous loop with automation at every transition point.

The bottom line: The ML lifecycle is not build-once-deploy-forever. It’s a living system that requires continuous investment in data, compute, monitoring, and iteration. Plan for the loop, not just the launch.

References

Google Cloud — Generative AI on Vertex AI Documentation. https://docs.cloud.google.com/vertex-ai/generative-ai/docs
Google Cloud — Generative AI Beginner’s Guide. https://docs.cloud.google.com/vertex-ai/generative-ai/docs/learn/overview
Google Cloud — Generative AI Leader Certification. https://cloud.google.com/learn/certification/generative-ai-leader
Google Cloud Skills Boost — Generative AI Leader Learning Path. https://www.skills.google/paths/1951

This is the final post in the Generative AI Foundations series. Read the full series: Part 1: The AI Hierarchy · Part 2: The Gen AI Landscape · Part 3: Data Quality

Vincent Bevia — corebaseit.com

BigQuery on Corebaseit — POS · EMV · Payments · AI

ML Lifecycle Stages — The Cycle That Never Stops

ML Lifecycle Stages — The Cycle That Never Stops

1. Data Ingestion and Preparation

2. Model Training

3. Model Deployment

4. Model Management

The Cycle Continues

References