At the beginning of November, Me and my colleague Honza attended the Space Application Hackathon where our team managed to win the earth observation category. Specifically, we worked out two use cases – classification of agricultural land use and crop yield prediction. Here is our write up from the event.
Are you into cluster-computing with Apache Spark? This year’s SAIS 2018 conference covered great data engineering and data science best practices for productionizing AI. In a nutshell, you should keep your training data fresh with stream processing, monitor quality, test and serve models (at massive scale when talking about Spark). The conference also provided some deep dive sessions on Spark integration with popular machine learning frameworks, such as well known TensorFlow, Scikit-learn, Keras, PyTorch, DeppLearning4j, BigDL and Deep Learning Pipelines.
Here is the list of several interesting topics (in case you couldn’t join;-):
Spark Experience and Use Cases
Great talk about Spark utilization for HEP (high energy psysics) data processing and analysis as a complementary tool for current rid computing in CERN.
“Imaging beyond imagination”
That is this year’s ICIP 2018 conference theme. I attended the world’s largest and most comprehensive technical conference focused on image and video processing and computer vision.
Here are my special picks:
Image Representation an Modeling
Reducing Anomaly Detection in Images to Detection in Noise
Smart approach to anomaly detection by removing self-similar content of the image – ready to use for detecting material defects, tumors and others!
This year’s ECCV 2018 conference experienced an unprecedented growth of community and brought to light the most recent advances in computer vision. As expected, all the sessions were dominated by Deep Learning with Convolutional Neural Networks (CNNs).
For those who couldn’t join, I picked up a few interesting topics that caught my attention. Here is the list:
This year we started to work on advanced analytical projects in manufacturing. The boom of IoT sensors, never-ending pressure to increase yields and output quality, decreasing marginal effect of lean and Six Sigma activities and the big trend of analytics caused that we quickly ran out of our existing capacities. The projects are intriguing, data are large, we are fun to work with and the demand is enormous. Honestly, I don’t see any reason why not to join us!