venerdì 1 giugno 2018

Predictive Analytics World for Industry 4.0 conference

If you are in Munich on June 12th and want to know how to make your IoT data streaming infrastructure easier to maintain, please try to attend my talk at the Predictive Analytics World for Industry 4.0. It will start at 3:30 PM. I hope to meet you there!

domenica 20 maggio 2018

Data Driven Innovation Open Summit 2018

Thank you for attending my talk at the Data Driven Innovation Open Summit 2018 in Rome. I will post to YouTube the videos I have prepared about the 2 demos of the SDC and SDC Edge originally planned for this talk as soon as I complete the audio comment and some small editing.

mercoledì 9 maggio 2018

Google I/O Extended 2018 Dublin

Thanks to all the people who attended my talk "The Journey to TensorFlow on the JVM Stack" at the Google I/O Extended 2018 in Dublin. The slide deck is available on my SlideShare space. More code examples would be available on this blog in the next months.

giovedì 12 aprile 2018

DataWorks Summit 2018, Berlin Edition: come to attend my talk.

AI, Machine Learning and Deep Learning are getting an hype nowadays even if most part of the algorithms and models at their core are around since long time:

1805 Least Squares
1812 Bayes' Theorem
1913 Markov Chains
1950 Turing's Learning Machine
1957 Perceptron
1967 Nearest Neighbor
1970 Automatic Differentiation
1972 TF-IDF
1980 Neocognitron
1981 Explanation Based Learning
1982 Recurrent Neural Network
1970 Back Propagation
1989 Reinforcement Learning
1995 Random Forest Algorithm
1995 Support Vector Machines
1997 LSTM

So what are the reasons that speed up and accelerated the implementation and made possible today for the theory to become reality?
There are several factors:
 - Cheaper computation: in the past hardware was a constraining factor for AI/ML/DL. Late advance in hardware (coupled with improved tools and software
frameworks) and new computational models (in particular around GPUs) have accelerated AI/ML/DL adoption.
 - Cheaper storage: the increased number of available data means more space needed for storage. Advance in hardware, cost reduction and improved performance made possible the implementation of new storage systems without the typical limitations of relational databases.
 - More advanced algorithms: less expensive compute and storage enable development and training of more advanced algorithms. As a result, DL is nowsolving specific problems like image classification or fraud detection with astonishing accuracy (and more sophisticated algorithms will continue to improve the state of the art).
 - More and bigger investments: investment in AI is no longer confined to universities or research institutes, but is done from many other entities such as tech giants, governments, startups and large enterprises across almost every industry sector.
 - Bigger data availability: AI/ML/DL need a huge amount of data to learn. The digital transformation of society is providing tons of raw material to fuel their advances. Big data coming from diverse sources such as IoT sensors, social and mobile computing, healthcare and many more new applications can be used to train models.
But often just getting data from any possible data source, in particular from the edge, requires moving mountains. Please attend my talk at the DataWorks Summit in Berlin on April 18th if you want to learn how to make edge data ingestion and analytics easier using a single tool, StreamSets Data Collector Edge, which is an ultralight, platform independent and small-footprint Open Source solution written in Go for streaming data from resource-constrained sensors and personal devices (like medical equipment or smartphones) to Apache Kafka, HDFS, Elastic Search and many other destinations.

martedì 6 marzo 2018

Java with The Best Conference

Do you believe that Java is dead? Please join the Java with the Best online conference on April 17th-18th and then probably you will change your mind. Of course if you are a Java technology passionate you need to attend as well ;) 2 days, 50 talks, 3 parallel sessions (Core Java, Big Data, Machine Learning and Cloud Development with Java, Java Frameworks, Libraries and Languages).  I hope you will get a chance to attend my talk: