Plato Data Intelligence.
Vertical Search & Ai.

Tag: Kaggle

Build, Share, Deploy: how business analysts and data scientists achieve faster time-to-market using no-code ML and Amazon SageMaker Canvas

Machine learning (ML) helps organizations increase revenue, drive business growth, and reduce cost by optimizing core business functions across multiple verticals, such as demand forecasting, credit scoring, pricing, predicting customer churn, identifying next best offers, predicting late shipments, and improving manufacturing quality. Traditional ML development cycles take months and require scarce data science and ML […]

Load and transform data from Delta Lake using Amazon SageMaker Studio and Apache Spark

Data lakes have become the norm in the industry for storing critical business data. The primary rationale for a data lake is to land all types of data, from raw data to preprocessed and postprocessed data, and may include both structured and unstructured data formats. Having a centralized data store for all types of data […]

Data Scientist vs. Data Engineer

The Background of Data Science Roles It was thought a few years ago that 2018 would amount a huge demand-supply gap in the Data Science market as supply would fail to keep pace with the rising demand for expert data scientists. However, the buzz from Gartner, which said more than 40 percent of Data Science […]

The post Data Scientist vs. Data Engineer appeared first on DATAVERSITY.

Introducing Text and Code Embeddings in the OpenAI API

We are introducing embeddings, a new endpoint in the OpenAI API that makes it easy to perform natural language and code tasks like semantic search, clustering, topic modeling, and classification. Embeddings are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships

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