zekeLabs Blog
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Practical use cases of AI in Business
zekeLabs
Artificial Intelligence is one of the most fascinating and bears much of the future in it. In fact, AI is the future of technology and businesses. Artificial intelligence with allied technologies like machine learning makes it possible for machines to impro...
Impact of Artificial Intelligence, Big Data and Technology on the Financial Sector: Disruption
zekeLabs
The financial services sector was one of the first sectors to understand the wave of new technology which included Artificial Intelligence (AI) and the promise of the Big Data Revolution. Businesses in this sector, define themselves by their ability to make...
The Machine Learning Pipeline - Essential things to know before getting started with machine learning
Awantik Das
Starting from development to deployment of machine learning, the journey of a product can be broken down to 7 important stages: Business Understanding, Data Wrangling, Visualization, Preprocessing, Model Training, Model Validation, Deployment
Big Data Processing with Deep Learning - Spark vs TensorFlow
Awantik Das
More and more organizations are integrating big data pipeline with deep learning infrastructure. This is something, Spark & TensorFlow folks have in mind as well. Let’s have a quick glance about their journey so far.
What is future prospects of being a Django developer in India?
Nalinee Choudhary
Apart from Training Django, due to increasing corporate requirement I am given assignments to interview candidates for Python & Django. Sharing my understanding of entire scenario from candidates prospective or corporate .
What are Big Data, Hadoop & Spark ? What is the relationship among them ?
Awantik Das
Big Data is a problem statement & what it means is the size of data under process has grown to 100's of petabytes ( 1 PB = 1000TB ). Yahoo mail generates some 40-50 PB of data every day. Yahoo has to read that 40-50 PB of data & filter out spans. E-commerce...
Top 3 Applications of Apache Spark
Awantik Das
Distributed computation got with induction of Apache Spark in Big Data Space. Lightning performance, ease of integration, abstraction of inner complexity & programmable using Python, Scala, Java & R makes it one of the . Here we are discussing most widely ...
Is it still necessary to understand map-reduce paradigms for machine learning on large data sets?
Awantik Das
Spark has covered a lot of mileage in a couple of years. And, with of Spark 2.0 - the internal implementation & optimization is abstracted the best possible way.
When Python has ML libraries, why do you need Apache Spark for analytics?
Awantik Das