Machine Learning and AI Integration in Databricks for Big Data
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 6 | Views: 340 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1

Informative Article | Engineering Science | India | Volume 12 Issue 2, February 2023 | Popularity: 5.2 / 10


     

Machine Learning and AI Integration in Databricks for Big Data

Ravi Shankar Koppula


Abstract: This paper explores the integration of machine learning (ML) and artificial intelligence (AI) within the Databricks platform, emphasizing its capabilities for big data analytics. Databricks, built on Apache Spark, provides a unified analytics environment that supports data science collaboration through interactive notebooks and scalable processing. The paper discusses the foundational concepts of machine learning and AI, the significance of data preparation, and the utilization of ML libraries such as MLLib and H2O within Databricks. Advanced techniques including deep learning and neural networks are examined, highlighting their practical applications in real-world scenarios. The integration of Databricks' tools with machine learning frameworks enables efficient data engineering and model deployment, offering a robust solution for enterprises to leverage big data for predictive analytics and decision-making.


Keywords: Databricks, Apache Spark, Machine Learning (ML), Artificial Intelligence (AI), Big Data, Data Preparation, MLLib, H2O, Deep Learning, Neural Networks


Edition: Volume 12 Issue 2, February 2023


Pages: 1754 - 1758


DOI: https://www.doi.org/10.21275/SR24819211633


Please Disable the Pop-Up Blocker of Web Browser

Verification Code will appear in 2 Seconds ... Wait



Text copied to Clipboard!
Ravi Shankar Koppula, "Machine Learning and AI Integration in Databricks for Big Data", International Journal of Science and Research (IJSR), Volume 12 Issue 2, February 2023, pp. 1754-1758, https://www.ijsr.net/getabstract.php?paperid=SR24819211633, DOI: https://www.doi.org/10.21275/SR24819211633

Top