Data Science, Artificial Intelligence, Analytics, and Machine Learning at the Enterprise scale are terms you've probably heard before. But what do they mean? We break it down for you in this blog. So, What Is Data Science? Data Science is a series of disciplines, technology, skills, expertise, and knowledge that encompass one thing: obtaining and preparing data for analysis.

Data lakes offer a number of advantages for machine learning, but it takes an experienced partner to unlock their full benefit. AI News caught up with Brian Flüg, Solutions Architect at Qubole, to find out how the company is helping data scientists with their workloads. What are the advantages of using a data lake for machine learning? The advantages of using a secure and open data lake for machine learning are numerous. It is simple to deploy and companies can reduce risk while decreasing costs.

Before going to the world of integration, machine learning, etc., I would like to discuss with all of you about a scenario many of you might experience when you live in a mega city. I lived in the London suburbs for almost 2 years (and it's a city quite close to my heart too), so let me use London as this story's background. When I moved to London, one question which came to my mind was whether I should buy a car or not. The public transport system in London is quite dense and amazing (Oh!!! I just love the amazing London Underground and I miss it in Toronto).

Snowflake and Qubole have partnered to bring a new level of integrated product capabilities that make it easier and faster to build and deploy machine learning (ML) and artificial intelligence (AI) models in Apache Spark using data stored in Snowflake and big data sources. In this second blog of three we cover how to perform advanced data preparation with Apache Spark to create refined data sets and write the results to Snowflake, thereby enabling new analytic use cases. The blog series covers the use cases directly served by the Qubole–Snowflake integration. The first blog discussed how to get started with ML in Apache Spark using data stored in Snowflake. Blogs two and three cover how data engineers can use Qubole to read and write data in Snowflake, including advanced data preparation, such as data wrangling, data augmentation, and advanced ETL to refine existing Snowflake data sets.

During the Deep Learning Summit at AWS re:Invent 2017, Terrence Sejnowski (a pioneer of deep learning) succinctly said, "Whoever has more data wins." He was echoing a premise that has been repeated many times in many ways by many people: machine learning requires big data to work. That's why here at Qubole we believe that enabling data scientists starts with giving them a platform to quickly select, clean, and aggregate datasets on a massive scale. The recent surge in impactful applications of deep learning algorithms has misled many people to believe that there has been a corresponding upswell in innovation in this field. Although there are indeed new bleeding-edge algorithms being released (most recently, Geoffrey Hinton's milestone capsule networks), most of the deep learning algorithms used in innovative technologies are actually decades old.

BigDL runs natively on Apache Spark, and because Qubole offers a greatly enhanced and optimized Spark as a service, it makes for a perfect deployment platform. In this Part 1 of a two-part series, you will learn how to get started with distributed Deep Learning library BigDL on Qubole. By the end, you will have BigDL installed on a Spark cluster with a distributed Deep Learning library readily available for you to use in your Deep Learning applications running on Qubole. In Part 2, you will learn how to write a Deep Learning application on Qubole that uses BigDL to identify handwritten digits (0 to 9) using a LeNet-5 (Convolutional Neural Networks) model that you will train and validate using MNIST database. Before we get started, here's some introduction and background on the technologies involved.

Last week, during the Deep Learning Summit at AWS re:Invent 2017, Terrence Sejnowski (a pioneer of deep learning) succinctly said "Whoever has more data wins". He was echoing a premise that has been repeated many times in many ways by many people: machine learning requires big data to work. Without large, well maintained training sets, machine learning algorithms--especially deep learning algorithms--fall far short of their potential. That's why here at Qubole we believe that enabling data scientists starts with giving them a platform to quickly select, clean, and aggregate datasets on a massive scale. The recent surge in impactful applications of deep learning algorithms has misled many people to believe that there has been a corresponding upswell in innovation in this field.

As the TechSparks countdown begins, we list the top Bengaluru startups that have grabbed the spotlight due to the work they do and the growth they are projecting. Bengaluru has always been referred to as India's Silicon Valley. This is not just in terms of the number of startups that have mushroomed in the city, but also keeping in mind the funding pumped into the Bengaluru startup ecosystem. According to YourStory data, between 2016 and 2017 (YTD), the total amount of funding raised by Bengaluru startups was a whopping $6.6 billion. As many as 460 startups have made Bengaluru their home in the past two years, be it e-commerce giant Flipkart, cab aggregator Ola or Practo, one of the top funded healthcare startups in India.

BigDL runs natively on Apache Spark, and because Qubole offers a greatly enhanced and optimized Spark as a service, it makes for a perfect deployment platform. In this Part 1 of a two-part series, you will learn how to get started with distributed Deep Learning library BigDL on Qubole. By the end, you will have BigDL installed on a Spark cluster with a distributed Deep Learning library readily available for you to use in your Deep Learning applications running on Qubole. In Part 2, you will learn how to write a Deep Learning application on Qubole that uses BigDL to identify handwritten digits (0 to 9) using a LeNet-5 (Convolutional Neural Networks) model that you will train and validate using MNIST database. Before we get started, here's some introduction and background on the technologies involved.