Why Java Will Dominate the Future of Machine Learning, AI, and Big Data

Great presentation by Bernard Traversat,  Head of the Java platform development, at Oracle Code One 2018, San Francisco. Here are key points:

  • Entire Big Data stack for storing data is written in Java (Hadoop, Spark, Kafka, Elastic Search, …)
  • Java has enormous ecosystem of tools and libraries
  • Enterprises run on Java
  • Bringing new technology into enterprise (such as AI, ML) is a huge challange
    because there is a lot to learn, and that gives Java and JVM huge advantage because enterprise allready knows Java and JVM. Technology needs to be understood by the enterprise.
  • Easy access and lower cost to develop, deploy and maintain:
    • easy access to developers
    • cost efficency (cost vs ROI)
    • maintainability as the code size is growing and aging (thanks to code readability)
  • Java is in the foundations of the cloud
  • Incoming Java innovations for ML, AI and BigData (openjdk.java.net):
    •  Panama, more efficent replacement for JNI (faster and easier communication with GPU).  Also the support for vector computing natively inside the JVM and Vector API
    • Valhalla, new type system for more efficient manipulation with data in memory
    • Loom, lightweight threads for massive parallel processing
    • ZGC, order of magnitude better performance for memory management
  • To conclude:
    • Java is #1 Programming Language
    • There are 12 Milion Java Developers
    • 38 Billion active JVM-s
    • 21 Billion cloud connected VM

The Most Significant JSR Award goes to Visual Recognition API, Code One, San Francisco

The most significant JSR Award, which is given by the official organization for Java technology standardization, Java Community Process (JCP) was awarded to Visual Recognition API, proposed and led by Zoran Sevarac (Deep Netts CEO), Java Champion Frank Greco (Crossroads Technologies), and Sandhya Kapoor (IBM Watson Architect). The proposal is official supported by IBM, and it is in First Early Draft Review phase. The award was announced during the Oracle Code One in San Francisco.
This award shows great interest and potentialfor development of visual recognition and machine learning technologies, and creating standards that would make it easier for Java developers to innovate and work with these technologies.

Machine Learning For Software Developers in 45 minutes, Code One, San Francisco

Machine Learning for Software Developers in 45 minutes at Oracle Code One conference in San Francisco, by Deep Netts CEO Zoran Sevarac and another Java champion Frank Greco,  (from Crossroads Technologies) was great success. It was full room of almost 300  Java developers looking for the fast way to get into the magic world of machine learning, but using what they allready know – Java, and skiping all the confusing parts for the beginners related to math formulas and statistics.
Judging by the comments and discussion after the session we have succeded in making fast introduction, and explaining essentials with hands on approach that typical Java developer need to get started and make next steps. The session included brief explanationof basic supervised machine learning techniques including:

  • Linear Regression
  • Logistic Regression
  • Feed Forward Neural Networks
  • Convolutional Neural Networks

This presentation will be the base for the series of blog posts on this topic.
Slides are available here

 

Practical Machine Learning at QCon New York 2018

QCon had an amazing machine learning  track that presented parctitioners from some of the biggest players in indutry like Pay Pal and Net Flix,  but also provided some real world experiences from building data science and machine learning teams, and adding machine learning skills to existing software development teams. Did you know that without using machine learning for fraud detection, Pay Pal would not be possible? Given the currrent scale, growth and rate of attacks, machine learning is the only way to secure the business.

At Net Flix they use an interesting approach for ensuring quality of their service, which is based on using machine learning techniques to augment the work of human engineers. The basic idea is not to leave everything to the machines (which is also not possible), but to use machine learning to help human engineers to detect and resolve issues more quickly.

It was very interesting to hear how about entire machine learning pipelines used at Door Dash to predict and improve food delivery. One of the key points I liked about this preseentations was advice about evolving machine learning models. In the beggining nobody has enough data, so start with something simple and improve model over time, as th eamount of data increase.

In a very interesting talk about team building by Sally Radwan, she discovered that once the company decided to include machine learning, that resulted in 60% of the team change in a period of one year. And interesting thing was that most of the developers left, while managerial positions were stable. That doesn’t have to be rule but be aware of the posibility.

Deep Netts platform was presented during a session for application performance tuning using deep learning, whics was very well attended and we got some very usefull feedback.

I had a great honour of hosting this track, working with all these great people and I learned a lot alonf the way. So thanks QCon for the great conference!