Artificial intelligence is the use of technologies to build machines and computers that can mimic cognitive functions (see images, listen to speech, understand text, make recommendations, etc.) associated with human intelligence.  Machine learning is a subset of AI that lets a machine learn from data without being explicitly programmed. 

Google Cloud Platform (GCP) offers a rich suite of AI and machine learning tools catering to users across different experience levels — from business analysts to seasoned ML engineers. Whether you’re analyzing structured data, classifying images, building custom deep learning models, or tapping into generative AI, there’s a GCP service tailored for you. 

In this expanded guide, you’ll learn: 

• Key AI/ML tools in Google Cloud 

• How to use them from the Cloud Console 

• Practical examples and sample code 

• Use cases, limitations, and best practices 

• A comprehensive summary table to guide your choices 

1. BigQuery ML 

BigQuery ML democratizes machine learning by enabling analysts to build models using standard SQL syntax directly within BigQuery. It’s ideal for use cases involving large, structured datasets — like customer churn prediction, sales forecasting, and classification tasks. 

Key Features 

• Supports classification, regression, time series, and clustering.

• Uses built-in SQL functions for training, evaluation, and prediction.

• No need to move data outside BigQuery.
  

Accessing BigQuery ML 

1. Navigate to BigQuery Studio - https://console.cloud.google.com/bigquery 

2. Open a project and select your dataset. 

3. Use the SQL workspace to run queries like CREATE MODEL or ML.PREDICT. 
   

Sample: Logistic Regression 

CREATE OR REPLACE MODEL `my_dataset.customer_churn_model` 
OPTIONS(model_type='logistic_reg') AS 
SELECT 
  tenure, 
  monthly_charges, 
  contract_type, 
  churn 
FROM 
  `my_dataset.customer_data`;

Best Practices 

• Normalize your numeric features before training.

• Use ML.EVALUATE to assess model performance.

• Partition of large datasets for efficient model training.

2. Vertex AI 

Vertex AI is GCP’s fully managed ML platform that provides a single UI and API for the complete ML lifecycle. It includes support for AutoML, custom model training, pipelines, feature store, and model deployment. 

a) Vertex AI AutoML 

AutoML simplifies model training by abstracting the heavy lifting of data preprocessing, feature selection, and hyperparameter tuning. It supports: 

• Tabular classification/regression 

• Image classification/object detection 

• Text sentiment/entity analysis 

• Video classification 

Accessing AutoML 

1. Go to Vertex AI Dashboard - https://console.cloud.google.com/vertex-ai 

2. Click “Train new model”. 

3. Choose your data type (tabular, image, text, etc.). 

4. Upload or link to your dataset. 

5. Follow the guided training wizard. 

Sample Code: Image AutoML (Python) 

from google.cloud import aiplatform 
 
aiplatform.init(project="your-project", location="us-central1") 
 
dataset = aiplatform.ImageDataset.create( 
    display_name="my-image-dataset", 
    gcs_source=["gs://your-bucket/images/"], 
    import_schema_uri=aiplatform.schema.dataset.ioformat.image.single_label_classification, 
) 
 
job = aiplatform.AutoMLImageTrainingJob( 
    display_name="image-classifier-job", 
    model_type="CLOUD", 
    multi_label=False, 
) 
 
model = job.run( 
    dataset=dataset, 
    model_display_name="my-image-model", 
    training_filter_split={"training_fraction": 0.8, "validation_fraction": 0.1, "test_fraction": 0.1}, 
) 

Limitations 

• Training time depends on data size and complexity.

• Less control over model internals.

b) Vertex AI Custom Training 

Custom training is for advanced users who want to use frameworks like TensorFlow, PyTorch, or XGBoost. You can train models using your own scripts in Docker containers or managed Jupyter environments. 

Accessing Custom Training 

1. In Vertex AI, go to Training → Custom Jobs. 

2. Choose your container or upload a training script. 

3. Specify machine specs (CPUs, GPUs, TPUs). 

Sample Code (Python SDK) 

from google.cloud import aiplatform 
 
aiplatform.init(project="your-project-id", location="us-central1") 
 
job = aiplatform.CustomTrainingJob( 
    display_name="custom-train-job", 
    script_path="train.py", 
    container_uri="gcr.io/cloud-aiplatform/training/tf-cpu.2-11:latest", 
    model_serving_container_image_uri="gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-11:latest" 
) 
 
model = job.run(replica_count=1, machine_type="n1-standard-4") 

Use Cases 

• NLP models using Transformers.

• Computer vision models with custom CNNs.

• Reinforcement learning pipelines.
  

3. Pre-trained APIs 

Google Cloud offers pre-trained APIs that let you access powerful AI capabilities with minimal setup. These are RESTful services available via API calls or SDKs. 

Key Services 

API	Capabilities
Vision API	Image labeling, OCR, object detection
Natural Language	Sentiment, syntax, entity recognition
Speech-to-Text	Audio transcription
Text-to-Speech	Audio generation from text
Translation	Language translation

Accessing Pre-trained APIs 

1. Go to API Libaray - https://console.cloud.google.com/apis/library 

2. Enable the required API. 

3. Create credentials (API key or service account). 

4. Use client libraries (Python, Node.js, Java, etc.) or REST calls. 

Sample: Vision API (Label Detection) 

from google.cloud import vision 
 
client = vision.ImageAnnotatorClient() 
 
with open("photo.jpg", "rb") as image_file: 
    content = image_file.read() 
 
image = vision.Image(content=content) 
response = client.label_detection(image=image) 
 
for label in response.label_annotations: 
    print(f"{label.description}: {label.score:.2f}") 

4. Generative AI with Gemini  

Google’s Gemini APIs power generative AI features such as chatbots, summarization, code completion, and document synthesis. These are hosted on Vertex AI with tools like Model Garden and Vertex AI Studio. 

Accessing Generative AI 

1. Visit Vertex AI Studio - https://console.cloud.google.com/vertex-ai/studio 

2. Use a prompt gallery or freeform chat interface. 

3. Choose a language model (Gemini Pro, Gemini Flash, etc.). 

4. For programmatic access, use vertexai Python SDK. 

Sample Code: Text Generation 

Use Cases 

• Automating customer service (chatbots).

• Creative writing or story generation. 

• Code suggestions or bug fixes.

5. Choosing the Right Tool 

Use Case	Recommended Service	Ideal User	Code Requirement	Notes
Predict outcomes with SQL	BigQuery ML	Data analysts	No	Great for structured data
Train models with minimal code	Vertex AI AutoML	Citizen developers	Low	Handles preprocessing, tuning
Train advanced ML/DL models	Vertex AI Custom	ML engineers	High	Use your own framework and logic
Extract insights from media/files	Pre-trained APIs	All developers	Low	Fastest way to use AI
Build chatbots or code generators	Generative AI (Gemini)	All developers	Low	Great for LLM and content generation tasks

6. Conclusion and Resources 

Google Cloud provides one of the most comprehensive, scalable, and user-friendly AI ecosystems available today. With services for every level of expertise, you can start with SQL in BigQuery ML and grow into training deep models in Vertex AI. Pair that with powerful APIs and generative tools — and you have everything you need to build production-ready AI. 

Helpful Links

• Vertex AI Documentation 

• BigQuery ML Documentation 

• AI/ML Product Overview 

• Google Cloud SDK 

Happy experimenting!