Apache Spark is one of the most useful tools for large scale data processing. It allows for data ingestion, aggregation, analysis and more on massive amounts of data and has been widely adopted by data engineers and other professionals.
With Spark Streaming and Spark SQL you can perform ETL operations in real-time on data coming from a variety of sources such as Kafka or Flume. And now if you want to do some basic machine learning, you can do that with SparkML, which is a library where they bring core statistical models like KMeans or decision tree models to users in a high level API.
But what if you want to analyze thousands of Tweets in real time, yet you don’t have a trained dataset to discover the sentiment of those tweets. Or maybe you want to classify documents on the fly or remove profanity from text or nudity from images?
Algorithmia’s over 4,000 pre-trained models and functions cover all of the above use cases and perfectly compliment Spark’s core functionality. These pre-trained models can easily integrate into Spark via a REST API endpoint. And just like Spark, Algorithmia has Python, R, Java, and Scala clients so you can stay in the language you’re familiar with while building robust machine and deep learning pipelines that scale with your data.
Lets play a game: can you tell the difference between these two sentences?
“Most of the time, travellers worry about their luggage.”
“Most of the time travellers worry about their luggage.”
Whoa, remove the comma and all of a sudden we’re having an entirely different conversation!
The little nuances of language can be hard enough for a human to understand, let alone a computer! How could we possibly teach a computer to understand the difference?
We host more than 4000 algorithms for over 50k developers. Here is a list of best practices we’ve identified for designing advanced algorithms. We hope this can help you and your team. Read More…
In this hands-on micro workshop, Jon will show you how to create a chatbot using Dexter, a company that makes building chatbots easy and accessible. Then he’ll show you how to make the chatbot emotionally aware using Algorithmia. Our open marketplace that hosts over 4,000 algorithms and microservices that are all available via a scalable API endpoint.
Jon will also go through some use cases covering why you would need a chatbot, especially one enabled with machine learning and provide some examples of other machine learning algorithms that work well in chatbots, but aren’t covered in the demo.
Please join us for a fun evening of food and drinks provided by Algorithmia and learn how to build an emotionally intelligent chatbot!
For more information or to RSVP check out the Seattle Building Intelligent Applications Meetup.