AI Fundamentals
a broad field which refers to the use of technologies to build machines and computers that can mimic cognitive functions associated with human intelligence.
Artificial intelligence
is a subset of AI that relies on various models to analyze large amounts of data, apply insights, and then make predictions and informed decisions. Algorithms improve performance over time as they are trained or exposed to more data.
Machine learning
a type of artificial intelligence that can produce new content, including text, images, audio, and synthetic data.
generative AI
This dimension of data quality refers to whether all the required information is present.
completeness
The dimension of data quality that gets confused
uniqueness
This dimension of data quality refers to whether or not the data is up-to-date and reflects the current state of the phenomenon that's being modeled.
timeliness
This dimension of data quality refers to whether the data conforms to a set of predefined standards and definitions, such as type and format.
validity
This dimension of data quality reflects the correctness of the data, such as the correct birth date or the accurate number of units sold.
accuracy
This dimension of data quality refers to whether the data is uniform and doesn't contain any contradictory information.
consistency
Google Cloud's set of tools and frameworks to help you understand and interpret predictions made by your machine learning models.
Explainable AI
A ML approach to understanding unstructured data like emails, images, videos, and audio.
Natural language processing
A ML enabled approach to surface product recommendations on their websites that are customized to individual users
Personalization
a tool for using SQL queries to create and execute machine learning models in BigQuery.
BigQuery ML
This option lets you use machine learning models that were built and trained by Google, so you don't have to build your own ML models if you don't have enough training data or sufficient machine learning expertise in house.
pre trained APIs, or application programming interfaces
a no code solution, letting you build your own machine learning models on Vertex AI through a point and click interface.
AutoML
code your very own machine learning environment, the training, and the deployment, which gives you flexibility and provides control over the ML pipeline.
custom training
brings together Google Cloud services for building ML under one unified user interface.
Vertex AI
chooses the best machine learning model for you by comparing different models and tuning parameters.
AutoML
has a flexible opensource ecosystem of tools, libraries, and community resources that enable researchers to innovate in ML and developers to build and deploy ML powered applications.
TensorFlow
provides models for speaking with customers and assisting human agents, increasing operational efficiency, and personalizing customer care to transform your contact center.
Contact Center AI
unlocks insights by extracting and classifying information from unstructured documents such as invoices, receipts, forms, letters, and reports.
Document AI
uses machine learning to select the optimal ordering of products on a retailer's e-commerce site when shoppers choose a category like winter jackets or kitchen ware.
Discovery AI
What is a lift-and-shift migration in cloud computing?
It involves moving an application or workload from an on-premises environment to the cloud without significant modifications, essentially "lifting" and "shifting" it.
This approach combines on-premises infrastructure with cloud-based resources, allowing organizations to leverage both environments based on specific needs and requirements.
Hybrid cloud migration
What is the main difference between capital expenditures (CapEx) and operating expenditures (OpEx)?
CapEx refers to upfront investments in physical assets, while OpEx refers to ongoing expenses for operational costs, such as cloud service subscriptions.