Remove APIs Remove Metrics Remove Workshop
article thumbnail

Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning

Workshops – In these hands-on learning opportunities, in 2 hours, you’ll be able to build a solution to a problem, and understand the inner workings of the resulting infrastructure and cross-service interaction. Builders’ sessions – These highly interactive 60-minute mini-workshops are conducted in small groups of fewer than 10 attendees.

APIs 95
article thumbnail

Transition your Amazon Forecast usage to Amazon SageMaker Canvas

AWS Machine Learning

You can also either use the SageMaker Canvas UI, which provides a visual interface for building and deploying models without needing to write any code or have any ML expertise, or use its automated machine learning (AutoML) APIs for programmatic interactions.

APIs 110
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning

It also enables you to evaluate the models using advanced metrics as if you were a data scientist. In this post, we show how a business analyst can evaluate and understand a classification churn model created with SageMaker Canvas using the Advanced metrics tab. The F1 score provides a balanced evaluation of the model’s performance.

Metrics 94
article thumbnail

Build your gen AI–based text-to-SQL application using RAG, powered by Amazon Bedrock (Claude 3 Sonnet and Amazon Titan for embedding)

AWS Machine Learning

Additionally, the complexity increases due to the presence of synonyms for columns and internal metrics available. I am creating a new metric and need the sales data. Start learning with these interactive workshops. In this post, we explore using Amazon Bedrock to create a text-to-SQL application using RAG.

article thumbnail

Introducing guardrails in Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Solution overview Knowledge Bases for Amazon Bedrock allows you to configure your RAG applications to query your knowledge base using the RetrieveAndGenerate API , generating responses from the retrieved information. An example query could be, “What are the recent performance metrics for our high-net-worth clients?”

APIs 130
article thumbnail

Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning

Workshops – In these hands-on learning opportunities, in the course of 2 hours, you’ll be able to build a solution to a problem, and understand the inner workings of the resulting infrastructure and cross-service interaction. Bring your laptop and be ready to learn! Reserve your seat now! Reserve your seat now! Reserve your seat now!

article thumbnail

Imperva optimizes SQL generation from natural language using Amazon Bedrock

AWS Machine Learning

The idea is to use metrics to compare experiments during development. Running predictions on the test set records results with the metrics needed to compare experiments. A common metric is the accuracy, which is the percentage of the correct results. For example, it can be used for API access, building JSON data, and more.