AWS Certified Machine Learning Engineer Associate MLA-C01
Involves the extraction and transformation of variables Feature Engineering
from raw data, such as price lists, product descriptions,
and sales volumes so that you can use features for training
and prediction
An input that machine learning (ML) models use during Model Feature
training and inference to make predictions
A set of practices that automate and simplify machine MLOps
learning (ML) workflows and deployments
An undesirable machine learning behavior that occurs Overfitting
when the machine learning model gives accurate
predictions for training data but not for new data
A machine learning (ML) technique that uses human Reinforcement Learning from Human Feedback (RLHF)
feedback to optimize ML models to self-learn more
efficiently.
A fully managed service that provides leading foundation Amazon Bedrock
models (FMs) and a set of features for rapidly developing
and scaling generative artificial intelligence (AI)
applications.
A type of generative artificial intelligence (AI). They Foundation Models (FMs)
produce human language instructions in response to one
or more inputs (prompts).
It is a technique for improving the quality and reliability of Retrieval Augmented Generation (RAG)
generative AI models by obtaining facts from external
sources.
An extensive language model designed for text generation. Amazon Titan Text G1 - Express
It is suitable for a variety of complex, general language
applications, including open-ended text generation and
conversational chat, as well as support for Retrieval
Augmented generation (RAG).
It is a sequence of characters. If the model finds a stop Stop sequences
sequence, it will cease creating new tokens. Different
models accept different sorts of characters in a stop
sequence, as well as varied maximum sequence lengths.
They may also permit the creation of numerous stop
sequences.
A large language models (LLMs) that have been trained to Text-to-text models
interpret massive amounts of textual data and human
language.
A machine learning technology that enables machines to Natural language processing (NLP)
understand and manipulate human language.
Utilizes a memory mechanism to store and apply Recurrent neural network (RNN)
information from previous inputs. This technique makes
RNNs useful for sequential data and tasks like natural
language processing, speech recognition, and machine
translation.
, AWS Certified Machine Learning Engineer Associate MLA-C01
It is a deep-learning architecture that includes an encoder Transformer
component that converts input text to embeddings.
It accepts natural language input and generate a high- Text-to-image models
quality image that corresponds to the supplied text
description. Text-to-image models include DALL-E 2 from
OpenAI, Imagen from the Google Research Brain Team,
Stable Diffusion from Stability AI, and Midjourney.
It is a deep learning architecture system that learns Diffusion architecture
through a two-step process. The first step is called forward
diffusion and second is called reverse diffusion.
It is an emerging field that focuses on developing, Prompt engineering
designing, and optimizing prompts to enhance the output
of LLMs for your needs.
It is a ML model that produces faster and easy to explain Simple model
results but may not be accurate.
It is a ML model that produces accurate results but may be Complex model
difficult to explain.
An unsupervised learning technique designed to represent Latent Dirichlet Allocation (LDA) Algorithm
a collection of documents as a combination of various
topics.
It is used to classify each pixel in an image into a class Semantic Segmentation Algorithm
from a predefined set of classes, effectively creating a
detailed segmentation map of the image.
It provides a streamlined and visual interface for data Data Wrangler
preparation and analysis.
It facilitates the thorough analysis and visualization of log Amazon CloudWatch Logs Insights
data, enabling the detection of patterns, identification of
performance constraints, and proficient troubleshooting of
issues.
Designed to build, train, and deploy machine learning Amazon SageMaker
models.
An NLP (natural language processing) service that Amazon Comprehend
analyzes text to extract insights like sentiment, key
phrases, and entities.
It is used to build conversational interfaces and chatbots. Amazon Lex
A data transport solution designed to accelerate the AWS Snowball
transfer of terabytes to petabytes of data to and from AWS
using secure storage appliances.
Involves the extraction and transformation of variables Feature Engineering
from raw data, such as price lists, product descriptions,
and sales volumes so that you can use features for training
and prediction
An input that machine learning (ML) models use during Model Feature
training and inference to make predictions
A set of practices that automate and simplify machine MLOps
learning (ML) workflows and deployments
An undesirable machine learning behavior that occurs Overfitting
when the machine learning model gives accurate
predictions for training data but not for new data
A machine learning (ML) technique that uses human Reinforcement Learning from Human Feedback (RLHF)
feedback to optimize ML models to self-learn more
efficiently.
A fully managed service that provides leading foundation Amazon Bedrock
models (FMs) and a set of features for rapidly developing
and scaling generative artificial intelligence (AI)
applications.
A type of generative artificial intelligence (AI). They Foundation Models (FMs)
produce human language instructions in response to one
or more inputs (prompts).
It is a technique for improving the quality and reliability of Retrieval Augmented Generation (RAG)
generative AI models by obtaining facts from external
sources.
An extensive language model designed for text generation. Amazon Titan Text G1 - Express
It is suitable for a variety of complex, general language
applications, including open-ended text generation and
conversational chat, as well as support for Retrieval
Augmented generation (RAG).
It is a sequence of characters. If the model finds a stop Stop sequences
sequence, it will cease creating new tokens. Different
models accept different sorts of characters in a stop
sequence, as well as varied maximum sequence lengths.
They may also permit the creation of numerous stop
sequences.
A large language models (LLMs) that have been trained to Text-to-text models
interpret massive amounts of textual data and human
language.
A machine learning technology that enables machines to Natural language processing (NLP)
understand and manipulate human language.
Utilizes a memory mechanism to store and apply Recurrent neural network (RNN)
information from previous inputs. This technique makes
RNNs useful for sequential data and tasks like natural
language processing, speech recognition, and machine
translation.
, AWS Certified Machine Learning Engineer Associate MLA-C01
It is a deep-learning architecture that includes an encoder Transformer
component that converts input text to embeddings.
It accepts natural language input and generate a high- Text-to-image models
quality image that corresponds to the supplied text
description. Text-to-image models include DALL-E 2 from
OpenAI, Imagen from the Google Research Brain Team,
Stable Diffusion from Stability AI, and Midjourney.
It is a deep learning architecture system that learns Diffusion architecture
through a two-step process. The first step is called forward
diffusion and second is called reverse diffusion.
It is an emerging field that focuses on developing, Prompt engineering
designing, and optimizing prompts to enhance the output
of LLMs for your needs.
It is a ML model that produces faster and easy to explain Simple model
results but may not be accurate.
It is a ML model that produces accurate results but may be Complex model
difficult to explain.
An unsupervised learning technique designed to represent Latent Dirichlet Allocation (LDA) Algorithm
a collection of documents as a combination of various
topics.
It is used to classify each pixel in an image into a class Semantic Segmentation Algorithm
from a predefined set of classes, effectively creating a
detailed segmentation map of the image.
It provides a streamlined and visual interface for data Data Wrangler
preparation and analysis.
It facilitates the thorough analysis and visualization of log Amazon CloudWatch Logs Insights
data, enabling the detection of patterns, identification of
performance constraints, and proficient troubleshooting of
issues.
Designed to build, train, and deploy machine learning Amazon SageMaker
models.
An NLP (natural language processing) service that Amazon Comprehend
analyzes text to extract insights like sentiment, key
phrases, and entities.
It is used to build conversational interfaces and chatbots. Amazon Lex
A data transport solution designed to accelerate the AWS Snowball
transfer of terabytes to petabytes of data to and from AWS
using secure storage appliances.
