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A data fabric solution must be capable of optimizing code natively using preferred programming languages in the data pipeline to be easily integrated into cloud platforms such as Amazon Web Services, Azure, Google Cloud, etc. This will enable the users to seamlessly work with code while developing data pipelines.
Microsoft Azure ML Platform The Azure Machine Learning platform provides a collaborative workspace that supports various programming languages and frameworks. You can define expectations about data quality, track data drift, and monitor changes in data distributions over time.
Data scientists can train large language models (LLMs) and generative AI like GPT-3.5 to generate natural language reports from tabular data that help human agents easily interpret complex dataprofiles on potential borrowers. Improve the accuracy of credit scoring predictions.
Data scientists can train large language models (LLMs) and generative AI like GPT-3.5 to generate natural language reports from tabular data that help human agents easily interpret complex dataprofiles on potential borrowers. Improve the accuracy of credit scoring predictions.
Microsoft AzureData Factory A cloud-based data integration service that allows users to create data-driven workflows for orchestrating data movement and transformation. AzureData Factory supports a wide range of data sources and offers advanced features for data transformation.
Data scientists can train large language models (LLMs) and generative AI like GPT-3.5 to generate natural language reports from tabular data that help human agents easily interpret complex dataprofiles on potential borrowers. Improve the accuracy of credit scoring predictions.
Attach a Common Data Model Folder (preview) When you create a Dataflow from a CDM folder, you can establish a connection to a table authored in the Common Data Model (CDM) format by another application. This path is essential for accessing and manipulating the CDM data within your Dataflow.
Cloud ETL Pipeline: Cloud ETL pipeline for ML involves using cloud-based services to extract, transform, and load data into an ML system for training and deployment. Cloud providers such as AWS, Microsoft Azure, and GCP offer a range of tools and services that can be used to build these pipelines.
Dbt allows you to define the sequence of operations and dependencies of your dbt models, essentially creating a workflow using your Snowflake data. However, if your data pipelines are completely within Snowflake, it will be more advantageous to simply use tasks within Snowflake to orchestrate them.
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