Data Preparation Data Warehouse SQL 
AWS Machine Learning Blog
OCTOBER 24, 2024
Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.
Data Science Dojo
JANUARY 12, 2023
When it comes to data, there are two main types: data lakes and data warehouses. What is a data lake? An enormous amount of raw data is stored in its original format in a data lake until it is required for analytics applications. Which one is right for your business? Let’s take a closer look.
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AWS Machine Learning Blog
OCTOBER 28, 2024
This minimizes the complexity and overhead associated with moving data between cloud environments, enabling organizations to access and utilize their disparate data assets for ML projects. You can use SageMaker Canvas to build the initial data preparation routine and generate accurate predictions without writing code.
Data Science Dojo
SEPTEMBER 11, 2024
These experiences facilitate professionals from ingesting data from different sources into a unified environment and pipelining the ingestion, transformation, and processing of data to developing predictive models and analyzing the data by visualization in interactive BI reports.
AWS Machine Learning Blog
APRIL 16, 2024
In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference.
DECEMBER 11, 2024
Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. The following screenshot shows an example of the unified notebook page.
phData
NOVEMBER 4, 2024
With data software pushing the boundaries of what’s possible in order to answer business questions and alleviate operational bottlenecks, data-driven companies are curious how they can go “beyond the dashboard” to find the answers they are looking for. One of the standout features of Dataiku is its focus on collaboration.
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