compute instances
compute clusters
inference clusters
attached compute.
what are the benefits of the object detection model ?
object detection is the form of ML that helps to recognize objects on the images. Each recognizable object will be putting in the bounding box with the class name and probability score.
Data pre-processing that involves various techniques like me scaling, normalization or feature engineering etc calls.
The confusion matrix provides a tabulated view of predicted and actual values for each class.
eg: if we are predicting the classification for 10 classes , our confusion matrix will have 10 x 10 size.
Feature selection helps us to narrow down the features that are important for our label prediction and discard all the features that dont play or play a minimal role in a label prediction . As a result our training model and prediction will be more efficient.
Five key elements of the Artifical Intelligence:
Machine learning: The foundation of AI SYSTEMS.
Anomaly detection : Tools and services for identification of the unusual activities.
computer vision : tools and services for understanding and recognizing objects in images, videos , faces and texts.
Natural language processing : tools and services for language understanding : text, speech, text analysis, and translation.
conversational AI : tools and services for intelligent conversation.
what are the six principles of responsible AI ?
Fairness, Reliability and safety, transparency , inclusiveness, accountabillity.
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how to predict numeric prediction ?
we can achieve this by creating a regression model based on the historical data of the things from previous quarters.
there are two types of machine learning :
1) supervised machine learning.
there are two parts of supervised machine learning : a) regression b) classification modelling types.
( this is the right one for the above ).
2) unsupervised machine learning.
only clustering model is related to the unsupervised machine learning.
Identify how data is defined and stored
Identify characteristics of relational and non-relational data.
Describe and differentiate data workloads.
Describe and differentiate batch and streaming data.
Identify how data is defined and stored
