Zeppelin is a web-based notebook for data engineers that enables data-driven, interactive data analytics with Spark, Scala, and more.
The project recently reached version 0.9.0-preview2 and is being actively developed, but there are still many things to be implemented.
One such thing is an API for getting comprehensive information about what's going on inside the notebook. There is already an API that completely solves the problems of high-level notebook management, but it doesn’t help if you want to do anything more complex.
This post is handling the following situation - how to setup up simple Mysql services with group replication being dockerized. In our case, we’ll take the latest Mysql (version 8.x.x)
FYI: all mentioned code (worked and tested manually) located here.
I will skip not interested steps like ‘what is Mysql, Docker and why we choose them, etc’. We want to set up possibly trouble proof DB. That’s our plan.
JavaCC 21 is a continuation of work on the venerable JavaCC parser generator, originally developed at Sun Microsystems in the 1990’s and released under a liberal open source license in 2003. It is currently the most advanced version of JavaCC. It has many feature enhancements (with more to come soon) and also generates much more modern, readable Java code. Also, certain key bugs have finally been fixed. (N.B. The “21” in JavaCC 21 is not a version number. It is simply part of the project name and means that this is a JavaCC for the 21st century!)
An eCommerce platform empowers startups, SMEs, and large enterprises to manage multiple online business processes such as website, marketing, sales, and operations.
The top eCommerce platforms handle online business tasks efficiently, and this finally helps enterprises in expanding their productivity.
With ML projects still on the rise we are yet to see integrated solutions in almost every device around us. The need for processing power, memory and experimentation has led to machine learning and DL frameworks targeting desktop computers first. However once trained, a model may be executed in a more constrained environment on a smartphone or on an IoT device. A particularly interesting environment to run the model on is browser. Browser-based solutions may be used on a wide range of devices, desktop and mobile, online and offline. The topic of this post is how to prepare a model for the in-browser usage.
This post presents an end-to-end implementations of a model creation in Python and Node.js. The end goal is to create a model and to use it in a browser. I'll use TensorFlow and TensorFlow.js as main frameworks. One could train a model in Python and convert it to JS. Alternative is to train a model directly in javascript, hence omitting the conversion step.
I have more experience in Python and use it in my everyday work. I occasionally use javascript, but have very little experience in the contemporary front-end development. My hope from this post that python developers with little JS experience could use it to kick start their JS usage.
We are proud to announce that we have built from ground up and released our high-quality (i.e. on par with premium Google models) speech-to-text Models for the following languages:
You can find all of our models in our repository together with examples, quality and performance benchmarks. Also we invested some time into making our models as accessible as possible — you can try our examples as well as PyTorch, ONNX, TensorFlow checkpoints. You can also load our model via TorchHub.
