If you want to make a head start in enterprise NLP, but have no clue about Spark, this article is for you. I have seen many colleagues wanting to step to this domain but disheartened due to the initial learning overhead that comes with Spark. It may seem inconspicuous at first glance since Spark code is a bit different than your regular Python script.
If you want to make a head start in enterprise NLP, but have no clue about Spark, this article is for you. I have seen many colleagues wanting to step to this domain but disheartened due to the initial learning overhead that comes with Spark. It may seem inconspicuous at first glance since Spark code is a bit different than your regular Python script. However, Spark and Spark NLP basics aren’t really hard to learn. If you axiomatically accept this assertion, I will show you how easy the basics are and will provide a road map to pave the way to learn key elements, which will satisfy most use cases of an intermediate level practitioner. Due to impeccable modularity that comes with Spark NLP pipelines, for an average learner, -mark my words- two weeks will be enough to build basic models. Roll up your sleeves, here we start!
Why Spark NLP?
Supply and Demand is the answer: It Is The Most Widely Used Library In Enterprises! Here are a few reasons why. Common NLP packages today have been designed by academics and they favor ease of prototyping over runtime performance, eclipsing scalability, error handling, target frugal memory consumption and code reuse. Although some libraries like ‘the industrial-strength NLP library — spaCy’ might be considered an exception (since they are designed to get things done rather than doing research), they may fall short of enterprise targets when it comes to dealing with data in volume.
Usage of tools designed for research and smaller scale tasks cannot be handy
We are going to have a different strategy here. Rather than following the crowds in the routine, we will use basic libraries to brush up ‘basics’ and then jump directly to address the enterprise sector. Our final aim is to target the niche by building continental pipelines, which are impossible to resolve with standard libraries, albeit their capacity in their league.
An extensively researched list of top Apache spark developers with ratings & reviews to help find the best spark development Companies around the world.
Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge.NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition
Welcome to the second part of the Spark NLP article. In the first part, the objective was to present an ice breaker for NLP practitioners and warm-up minds towards Spark NLP. The strongest bias against any Spark-based library comes from the school of thought that states “Spark code is a bit different than your regular Python script”. To fight this prejudice, learning strategies were shared, and if you have followed them, you are ready for the next level.
In today’s world we have a lot of data. And this data will only grow more and more in future. According to a study, in 2020, the data produced is abound 44 zettabytes each day. And by 2025, approximately 463 exabytes would be created every 24 hours worldwide. Do you ever imagine how one can store or process this much data ?A solution to this is Apache Spark and in this blog I’m going to discuss about Spark Streaming here.
Using Spark NLP with Jupyter notebook for natural language processing in Docker environment. As described in , Docker is a tool that allows us to easily deploy applications (e.g., Spark NLP) in a sandbox (called container) to run on any Docker supported host operating system (i.e., Mac).