Development

rotor is a non-intrusive event loop friendly C++ actor micro framework, similar to its elder brothers like caf and sobjectizer. The new release came out under the flag of pluginization, which affects the entire lifetime of an actor.

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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.

What is full-stack development?

What qualities does the ideal full-stack developer possess?

• Mastery over front-end technologies
• Knowledge of at least one server-side programming language
• Concrete understanding of DBMS technology
• UI/UX design skills
• Experience in handling servers
• Knowledge of Git and version control systems
• In-depth understanding of web services or API
• Awareness of security concerns and frameworks
• Understanding of algorithms and data structure

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.

Challenges of real-time AI/ML computations

• A “high-load” customer portal is integrated with an online recommendation system. The plan is to reconfigure promo campaigns at the level of the entire retail network (we will assume that instead of a “flat” promo campaign master there will be used a “segment-tactic” matrix). What will happen to the recommender mechanisms? What will happen to data feeds and updates into the recommender mechanisms (the volume of input data having increased 25000 times)? What will happen to recommendation rule generation setup (the need to reduce 1000 times the recommendation rule filtering threshold due to a thousandfold increase of the volume and “assortment” of the rules generated)?
• An equipment health monitoring system uses “manual” data sample feeds. Now it is connected to a SCADA system that transmits thousands of process parameter readings each second. What will happen to the monitoring system (will it be able to handle equipment health monitoring on a second-by-second basis)? What will happen once the input data receives a new bloc of several hundreds of columns with data sensor readings recently implemented in the SCADA system (will it be necessary, and for how long, to shut down the monitoring system to integrate the new sensor data in the analysis)?
• A complex of AI/ML mechanisms (recommendation, monitoring, forecasting) depend on each other’s results. How many man-hours will it take every month to adapt those AI/ML mechanisms’ functioning to changes in the input data? What is the overall “delay” in supporting business decision making by the AI/ML mechanisms (the refresh frequency of supporting information against the feed frequency of new input data)?