Name: Apache® Spark™ MLlib: From Quick Start to Scikit-Learn
Start: 2016-02-24T18:00:00Z
End: 2016-02-24T18:58:33.000Z
Location: BrightTALK
Rating: 4.6

No matter at what stage of your data journey you’re in, this channel will help you get a better understanding of the fundamental concepts of the Databricks Lakehouse platform and the problems we’re helping to solve for data teams.

Industry track: Financial Servies.
Presented by Naman Joshi, covering risk and compliance.

Risk & Compliance - Presented by Naman Joshi

During this keynote, you will hear directly from Databricks what innovating with Data + AI means.

Industry Leadership Forum Keynote: Innovating With Data + AI

Financial Services Solution Showcase - Data + AI Demo:
Creating deep customer connections through Hyper-personalized

Hyper-personalization Financial Services - Demo

Solution Showcase: Data + AI Demos
Personalization and Recommendations

M&E Personalization and Recommendations demo

Customer Behavioural Segmentation. Presented by Dan Morris

Segmentation Demo with Dan Morris

This is the complete recording of the media and entertainment track of our recent Asia-Pacific Industry Leadership Forum

Lakehouse for Media and Entertainment - Full Video

This is the complete recording of 'Lakehouse for retail and e-commerce' as part of the Databricks Industry leadership forum.

Lakehouse for retail and e-commerce

Solution Showcase: Data + AI Demo:
Customer Lifetime Value

Customer Lifetime Value - Demo orientation

This is the complete recording of the financial services session, as part of the Innovating With Data + AI Asia-Pacific Industry Leadership Forum

Industry Leadership Forum - FINANCIAL SERVICES SESSION

Lakehouse for Retail - Demand forecasting

eCloudvalley - Demand Forecasting

Customer Panel: The Future of Warehousing
Moderated by Joel Minnick - VP of Product Marketing, Databricks. You will hear from Dream11, Liven and Grab

Customer Panel: The Future of Warehousing

This is the complete recording of the general session, as part of the Innovating With Data + AI
Asia-Pacific Industry Leadership Forum

Industry Leadership Forum - GENERAL SESSION

Dream11 Keynote - Personalizing Fantasy Sports with Data + AI

Dream11 is the world’s largest fantasy sports platform, with over 120 million users playing fantasy cricket, football, kabaddi, basketball, hockey, volleyball, handball, rugby, futsal, American football and baseball. The platform manages about 100 million requests per minute, with more than 16 terabytes of information and billions of data points per day. With phenomenal volumes of user activity and engagement on its platform, Dream11 has been on a mission to raise the bar for driving adoption as well as personalizing its fantasy sports experience for users at scale. But without scalable machine learning capabilities, they were unable to tap into the promise of their data to create next-level fantasy sporting experiences. By leveraging Databricks, Dream11 aims to democratize data across teams, foster seamless collaboration to improve productivity and innovation, and deliver on customer-centric solutions that help them stay ahead of the curve in critical areas ranging from fraud detection to data security and personalization.

Dream11 Keynote - Personalizing Fantasy Sports with Data + AI

Databricks is the Data and AI company. As the first and only Lakehouse Platform in the cloud, Databricks unifies all your data, analytics, and AI on a simple, open, and collaborative platform.

Databricks Lakehouse Video

Organisations are looking to streamline their data management by melding their data warehouse and data lake into a single data lakehouse. A lakehouse provides a highly performant and reliable single source of data at a lower cost.
Learn how digital native businesses, Cascade and Liven, have used the Databricks Lakehouse Platform to:
- Address multiple data science use cases — including recommendation models, logistics and demand forecasting — as well as fraud detection
- Enable their business analysts to access dashboards to view business performance and address issues immediately
- Have a single automated system to manage their data pipelines to support these use cases and more

Building a Data Lakehouse at Cascade and Liven (Feb 2022)

At Databricks, our mission is to make big data simple. We solve the conundrum of how to store, manage and get insight from the sheer variety of data types that exist today using a simple, open and collaborative cloud data platform that we refer to as the Databricks Lakehouse. 

No matter at what stage of your data journey you’re in, this webinar series will help you get a better understanding of the fundamental concepts of the Databricks Lakehouse platform and the problems we’re helping to solve for data teams in being able to work collaboratively and be more productive using one common platform for all data uses-cases.

Introduction to Databricks Lakehouse (Non-technical overview)

Lakehouse is the modern data management paradigm that enables companies to get the most out of their data by leveraging the best of data lakes and data warehouses. This session walks through the evolution of data management and how the lakehouse model enables teams to work in a unified system that provides performant queries, support for streaming, data science, machine learning and BI capabilities -- all built on a single source of truth.

We will also explore Delta Lake - an open source storage layer that brings reliability to data lakes - enabling ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.

Introduction to Databricks Lakehouse (Technical Overview)

As organisations continue to become more data-driven, a collaborative environment for easier access and visibility into the data, models trained against the data, reproducibility, and insights uncovered within the data is critical.


Explore how Databricks lets data scientists and ML engineers quickly move from experimentation to production-scale machine learning model deployments — all on the same platform. In this workshop, we’ll work with a single data set throughout the lifecycle as well as scikit-learn, MLflow and Apache Spark™ on Databricks.

A Guide to Data Science using Databricks

In this webcast, Joseph Bradley from Databricks will be speaking about Apache Spark’s distributed Machine Learning Library - MLlib.

We will start off with a quick primer on machine learning, Spark MLlib, and a quick overview of some Spark machine learning use cases.  We will continue with multiple Spark MLlib quick start demos.   Afterwards, the talk will transition toward the integration of common data science tools like Python pandas, scikit-learn, and R with MLlib.

Apache® Spark™ MLlib: From Quick Start to Scikit-Learn

Spark

Machine

Learning

MLlib

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Apache® Spark™ MLlib: From Quick Start to Scikit-Learn

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