We’ve broken down the resources by what we think a Data Analyst, Data Scientist, ML Engineer, or a Software Engineer might be most interested in. But we also recognize there’s a lot of overlap between these roles, so even if you identify as a Data Scientist, for example, you might find some of the resources for ML Engineers or Developers just as useful!

Data Analyst

From data to insights, and perhaps some modeling, data analysts look for ways to help their stakeholders understand the value of their data.

Data exploration and Feature Engineering

• [Guide] Exploratory Data Analysis for Feature Selection in Machine Learning
• [Documentation] Feature preprocessing in BigQuery 

Data visualization

• [Guide] Visualizing BigQuery data using Data Studio
• [Blog] Go from Database to Dashboard with BigQuery and Looker

Data Scientist

As a data scientist, you might be interested in generating insights from data, primarily through extensive exploratory data analysis, visualization, feature engineering, and modeling. If you’d like one place to start, check out Best practices for implementing machine learning on Google Cloud.

Model registry

• [Video] AI/ML Notebooks how to with Apache Spark, BigQuery ML and  Vertex AI Model Registry

Model training

• [Codelab] Train models with the Vertex AI Workbench notebook executor
• [Codelab] Use autopackaging to fine tune Bert with Hugging Face on Vertex AI Training
• [Blog] How To train and tune PyTorch models on Vertex AI

Large scale model training

• [Codelab] Multi-Worker Training and Transfer Learning with TensorFlow
• [Blog] Optimize training performance with Reduction Server on Vertex AI
• [Video] Distributed training on Vertex AI Workbench 

Model tuning

• [Codelab] Hyperparameter tuning
• [Video] Faster model training and experimentation with Vertex AI

Model serving

• [Blog] How to deploy PyTorch models on Vertex AI
• [Blog] 5 steps to go from a notebook to a deployed model

ML Engineer

Below are resources for an ML Engineer, someone whose focus area is MLOps, or the operationalization of feature management, model serving and monitoring, and CI/CD with ML pipelines.

Feature management

• [Blog] Kickstart your organization’s ML application development flywheel with the Vertex Feature Store
• [Video] Introduction to Vertex AI Feature Store

Model Monitoring

• [Blog] Monitoring feature attributions: How Google saved one of the largest ML services in trouble

ML Pipelines

• [Blog] Orchestrating PyTorch ML Workflows on Vertex AI Pipelines
• [Codelab] Intro to Vertex Pipelines
• [Codelab] Using Vertex ML Metadata with Pipelines

Machine Learning Operations

• [Guide] MLOps: Continuous delivery and automation pipelines in machine learning

Software Engineer with ML applications

Here are some resources if you work more as a traditional software engineer who spends more time on using ML in applications and less time on data wrangling, model building, or MLOps.

• [Blog] Find anything blazingly fast with Google’s vector search technology
• [Blog] Using Vertex AI for rapid model prototyping and deployment
• [Video] Machine Learning for developers in a hurry

Looking for resources?

Are you looking for more information but you can’t seem to find them? Let us know! Reach out to us on Linkedin:

• Nikita Namjoshi
• Polong Lin