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Who is Domino?

Domino Data Lab empowers the largest AI-driven enterprises to build and operate AI at scale. Domino’s Enterprise AI Platform provides an integrated experience encompassing model development, MLOps, collaboration, and governance. With Domino, global enterprises can develop better medicines, grow more productive crops, develop more competitive products, and more. Founded in 2013, Domino is backed by Sequoia Capital, Coatue Management, NVIDIA, Snowflake, and other leading investors.

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  • Agentic AI
  • AI Governance
  • Airflow
  • Anaconda
  • Apache Spark
  • Artificial Intelligence
  • Clustering
  • Dask
  • Data Science
  • Density-based clustering
  • dplyr
  • Factor analysis
  • Feature
  • Feature Engineering
  • Feature Extraction
  • Feature selection
  • Folium
  • GenomicRanges
  • ggmap
  • ggplot
  • GPU
  • Ground Truth
  • Hash table
  • Hyperparameter Tuning
  • Interpretability
  • Jupyter Notebook
  • Kubernetes
  • LLMOps
  • Machine Learning
  • Machine Learning Algorithms
  • MLOps
  • Model Drift
  • Model Evaluation
  • Model monitoring
  • Model Selection
  • Model Tuning
  • Overfitting
  • Plotly
  • PySpark
  • PyTorch
  • Responsible AI
  • Shiny (in R)
  • sklearn
  • spaCy
  • SR 26-2
  • Statistical Computing Environment (SCE)
  • TensorFlow
  • Underfitting
  • XGBoost

    • Dask

    What is Dask?

    Dask was released in 2018 to create a powerful parallel computing framework that is extremely usable to Python users, and can run well on a single laptop or a cluster. Dask is lighter weight and easier to integrate into existing code and hardware than Apache Spark.

    Dask is a flexible library for parallel computing in Python. Dask is composed of two parts:

    1. Dynamic task scheduling optimized for computation and interactive computational workloads. The central dask-scheduler process coordinates the actions of several dask-worker processes spread across multiple machines and the concurrent requests of several clients.
    2. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to distributed environments. These parallel collections run on top of dynamic task schedulers.

    Internally, Dask encodes algorithms in a simple format involving Python dicts, tuples, and functions. This graph format can be used in isolation from the Dask collections. Working directly with task graphs is rare, unless you intend to develop new modules with Dask.

    Dask diagram
    Dask diagram

    Source: Dask Documentation

    Since Dask supports Pandas dataframes and NumPy array data structures, data scientists can continue using the tools they know and love. Dask also integrates tightly with Scikit-learn’s JobLib parallel computing library that enables parallel processing of Scikit-learn code with minimal code changes.