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  2. Databricks - Wikipedia

    en.wikipedia.org/wiki/Databricks

    Databricks, Inc. is a global data, analytics, and artificial intelligence (AI) company, founded in 2013 by the original creators of Apache Spark. [ 1 ] [ 4 ] The company provides a cloud-based platform to help enterprises build, scale, and govern data and AI, including generative AI and other machine learning models.

  3. Query optimization - Wikipedia

    en.wikipedia.org/wiki/Query_optimization

    The query optimizer attempts to determine the most efficient way to execute a given query by considering the possible query plans. [ 1 ] Generally, the query optimizer cannot be accessed directly by users: once queries are submitted to the database server, and parsed by the parser, they are then passed to the query optimizer where optimization ...

  4. Star schema - Wikipedia

    en.wikipedia.org/wiki/Star_schema

    Star schema used by example query. Consider a database of sales, perhaps from a store chain, classified by date, store and product. The image of the schema to the right is a star schema version of the sample schema provided in the snowflake schema article.

  5. Databricks CEO thinks we’re on the verge of an ‘intelligence ...

    www.aol.com/finance/databricks-ceo-thinks-verge...

    Databricks is also considered a top candidate to go public. “Right now we have a huge demand on our business and we're focused on satisfying that. When the time is right, we will also go public.

  6. Simulation-based optimization - Wikipedia

    en.wikipedia.org/wiki/Simulation-based_optimization

    For example, there might be too many possible values for input variables, or the simulation model might be too complicated and expensive to run for a large set of input variable values. In these cases, the goal is to iterative find optimal values for the input variables rather than trying all possible values.

  7. Test functions for optimization - Wikipedia

    en.wikipedia.org/wiki/Test_functions_for...

    In applied mathematics, test functions, known as artificial landscapes, are useful to evaluate characteristics of optimization algorithms, such as convergence rate, precision, robustness and general performance.

  8. Hyperparameter optimization - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_optimization

    In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.

  9. Program optimization - Wikipedia

    en.wikipedia.org/wiki/Program_optimization

    For example, the task of sorting a huge list of items is usually done with a quicksort routine, which is one of the most efficient generic algorithms. But if some characteristic of the items is exploitable (for example, they are already arranged in some particular order), a different method can be used, or even a custom-made sort routine.