AWS SageMaker Complete Course| PyTorch & Tensorflow NLP-2023

bring a custom docker container from your local machine, push it to SageMaker, and train and deploy a transformer-based classification model with input, model, output folders, and an endpoint.

Learn to convert an existing algorithm into a docker image and container, run it on a local machine, and push the image to SageMaker for text classification using a transformer.

Create and deploy a Docker image and container by writing a Dockerfile, selecting a Ubuntu base, installing Python and libraries, and running a training script to evaluate model performance.

Create a dockerfile and build a compatible docker image for AWS SageMaker by selecting a compatible base image, installing training libraries, and organizing code for deployment.

Learn to build and deploy a Docker image for SageMaker, create a container, push to S3, and train via notebook or console, achieving about 80.3% accuracy.

Learn to build a Docker image, configure the full image name with account and registry, log in to AWS ECR, and push the image to a repository.

Troubleshoot docker container push issues in SageMaker by checking the IAM role and attaching the EC2 full access policy, then learn to consume the pushed image and train the model.

Learn to bring your Docker container to SageMaker, configure account, region, image and tag, run training, monitor accuracy, and deploy a trained model to an endpoint for prediction.

Train a multiclass classification model from preprocessing to local training, then containerize it with Docker and push the image for stage deployment across two sessions.

Train a multiclass text classifier using a BERT-based model inside a SageMaker container. Implement preprocessing, tokenization, attention masks, and batched training with dropout, optimizer, and checkpointing.

Build and run a bert-based sentiment analysis inside a docker container by creating a dockerfile and installing conda, transformers, and pytorch, then prepare for SageMaker deployment.

Bring your BERT docker container, part 4, to demonstrate preparing a BERT-based pipeline, installing transformers and vision dependencies, handling image inputs, and building a notebook workflow.

Learn to create a BERT Docker container in AWS SageMaker, setting up notebooks, uploading data, building, and pushing the image.

Learn to build and push a BERT docker container to AWS SageMaker using a shell script, configuring account and region, tagging the image, and pushing to the repository.

Pull and import the docker image into a stage maker and trainer model, then configure account number and region, select an instance type, and deploy to an endpoint.

Learn to containerize a custom SageMaker algorithm by encapsulating code in functions, configuring a Dockerfile, defining an entry point, and organizing input, configuration, model, and output folders.

Explore end-to-end deployment of a random forest classifier in AWS SageMaker, including dataset upload, a 70/30 train-test split, model evaluation with accuracy metrics, and converting models for production.

Train and evaluate a random forest classifier by loading data, performing a train-test split, tuning hyperparameters, and serializing the model for endpoint deployment.

Convert your custom algorithm into the AWS SageMaker format, containerize it with a docker image, register it in a container registry, and integrate with SageMaker workflows.

Build a random forest model to predict heart disease using features such as blood pressure, cholesterol, and gender, with a 70/30 train-test split.

Move training and testing data into an S3 bucket, create a docker container, and set up a stage maker for deploying a custom algorithm.

Deploy a trained model to an endpoint, first locally for a demo, then to hosting, call it from a host, and evaluate with a confusion matrix and classification report.

Explore building a linear regression model in AWS SageMaker using a pre-built algorithm, register and deploy it to an endpoint, and run real-time and batch predictions.

Set up a SageMaker notebook instance, configure an IAM role and S3 buckets, upload training and testing data, and begin linear regression with a pre-built SageMaker algorithm.

Explore the SageMaker pre-built linear regression workflow, from dataset preparation and exploratory analysis to train test splits and training with S3 storage.

Learn to train a SageMaker pre-built linear regression model, transfer data from a local instance to S3, configure buckets and instance types, and organize train, test, and model artifacts.

Explore SageMaker's pre-built linear regression algorithm by loading data from S3, configuring training on a chosen instance, and evaluating performance using MSE.

Explore how to use SageMaker pre-built logistic regression to train, evaluate, and deploy a model with data from an S3 bucket on a diabetes dataset and feature analysis.

Learn to build and deploy a SageMaker pre-built logistic regression model: upload training and test data to S3, configure training, and deploy an endpoint for binary or multiclass classification.

Learn to deploy any model in sage maker, whether prebuilt, your own, or a docker container, by training, selecting instance type, and creating an endpoint with configuration for online predictions.

Learn to perform model predictions by invoking a deployed endpoint from a stage, feed a dataset from S3, convert data frames to arrays, and output prediction scores.

Create and automate a SageMaker pipeline from data prep to deployment, enabling scheduled notebook retraining and end-to-end workflow management.

Create an AWS step function pipeline that ties data preparation, training, and deployment in SageMaker, and learn to set up a notebook and IAM roles for scheduling.

Build a SageMaker pipeline by configuring step functions, IAM roles, and S3 storage; preprocess data, split into training and testing sets, and train logistic regression model with a prebuilt algorithm.

Create a SageMaker training pipeline and workflow by integrating training, model, and transform steps into a single workflow, deploy endpoint, and schedule the step function with cloud watch.

Schedule a SageMaker notebook pipeline with CloudWatch events and a step function to train, transform data, and deploy an endpoint in an automated ML lifecycle.