You will get BERT model using AWS SageMaker
Project details
Hi, I am a Data Scientist that uses Amazon SageMaker to build and train ML models to deploy models to production. This package deals with mainly NLP Text analysis, Sentiment Analysis.
What this package offers:
+ Feature transformation with processing job and Feature Store.
- Preparing raw dataset to train a BERT model.
- Classify customer reviews into positive (1), neutral (0) and negative (-1) sentiment.
- Perform required feature transformation with a SageMaker processing job, which will be running a custom Python script.
+ Train a review classifier with BERT
- Configure dataset, hyper-parameters and evaluation metrics
- Build PyTorch model run as a SageMaker Training Job.
- Deploy and Test the model.
+ Build a Pipeline to train and deploy a BERT-Based text classifier
- Define and run a pipeline using a directed acyclic graph (DAG)
- Define a processing step that cleans, balances, transforms, and splits our dataset into a train, validation, and test dataset
- Define a training step that trains a model using the train and validation datasets
- Define a processing step that evaluates the trained model's performance on the test dataset.
What this package offers:
+ Feature transformation with processing job and Feature Store.
- Preparing raw dataset to train a BERT model.
- Classify customer reviews into positive (1), neutral (0) and negative (-1) sentiment.
- Perform required feature transformation with a SageMaker processing job, which will be running a custom Python script.
+ Train a review classifier with BERT
- Configure dataset, hyper-parameters and evaluation metrics
- Build PyTorch model run as a SageMaker Training Job.
- Deploy and Test the model.
+ Build a Pipeline to train and deploy a BERT-Based text classifier
- Define and run a pipeline using a directed acyclic graph (DAG)
- Define a processing step that cleans, balances, transforms, and splits our dataset into a train, validation, and test dataset
- Define a training step that trains a model using the train and validation datasets
- Define a processing step that evaluates the trained model's performance on the test dataset.
What's included
| Service Tiers |
Starter
$20
|
Standard
$30
|
Advanced
$45
|
|---|---|---|---|
| Delivery Time | 1 day | 3 days | 4 days |
Number of Revisions | 1 | 1 | 1 |
Number of Model Variations | 1 | 2 | 2 |
Number of Scenarios | 1 | 1 | 2 |
Number of Graphs/Charts | 1 | 1 | 1 |
Model Validation/Testing | |||
Model Documentation | - | - | - |
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$5 - $10
Additional Graph/Chart
(+ 1 Day)
+$5About Kyle
AI Researcher
Johannesburg, South Africa - 12:16 pm local time
My proficiency in data analysis, model evaluation, and optimization ensures the delivery of impactful insights and solutions.
Steps for completing your project
After purchasing the project, send requirements so Kyle can start the project.
Delivery time starts when Kyle receives requirements from you.
Kyle works on your project following the steps below.
Revisions may occur after the delivery date.
Feature transformation
Preparing raw dataset to train for BERT model
Review classifier with BERT
Configure dataset, hyper-parameters and evaluation metrics