Skip to main content

Googlielmo's Blog 2.0: a Fresh Restart

After a 7 months hiatus I have decided to go back posting on this blog. Lot of things happened across 2020 and 2021 that left me with little or no time at all to share my thoughts and findings. In this long period of time I have been involved in challenging ML/AI projects, managing them and interacting with people 100% remotely because of the COVID-10 pandemic, had a chance to experiment with many and in some cases successfully applications of new DL architectures and Python Open Source libraries, but also tune mine and my family personal life among all the style changes imposed by the pandemic.

The reasons that led me to restart the blog are mostly the following:

  • I have accumulated tons of technical topics that are worth to share with a wider audience. During the past months I have shared some through social networks such as LinkedIn and Twitter or in few virtual meetups or conferences, but they need more deep dive.
  • This week I gave a workshop at the ODSC Europe 2021 conference and I had a chance to do virtual networking with several people and many conversations I had there made me aware that some topics that to me are trivial couldn't be for others, so sharing my hands-on experience on them would be of help.
  • Currently there is a lot of unnecessary hype about ML/AI due to bad press, ignorance on the domain matter, confusion, disconnection between academy and industry, tech vendors false promises, etc. So, I would like to help people by sharing my experience in real life ML/AI projects and debunk false myths about it.

While in the past I used to post on this blog with no regular cadence, from now on I have decided to post twice a week (no fixed days or times). Topics would be not only technical, but would also cover other related matters, such as remote working, gaps between academy and industry, AI ethics, etc.

Have a great day and hope you're going to follow this blog and share your feedback about the topics that would be presented here.

Comments

Post a Comment

Popular posts from this blog

Exporting InfluxDB data to a CVS file

Sometimes you would need to export a sample of the data from an InfluxDB table to a CSV file (for example to allow a data scientist to do some offline analysis using a tool like Jupyter, Zeppelin or Spark Notebook). It is possible to perform this operation through the influx command line client. This is the general syntax: sudo /usr/bin/influx -database '<database_name>' -host '<hostname>' -username '<username>'  -password '<password>' -execute 'select_statement' -format '<format>' > <file_path>/<file_name>.csv where the format could be csv , json or column . Example: sudo /usr/bin/influx -database 'telegraf' -host 'localhost' -username 'admin'  -password '123456789' -execute 'select * from mem' -format 'csv' > /home/googlielmo/influxdb-export/mem-export.csv

jOOQ: code generation in Eclipse

jOOQ allows code generation from a database schema through ANT tasks, Maven and shell command tools. But if you're working with Eclipse it's easier to create a new Run Configuration to perform this operation. First of all you have to write the usual XML configuration file for the code generation starting from the database: <?xml version="1.0" encoding="UTF-8" standalone="yes"?> <configuration xmlns="http://www.jooq.org/xsd/jooq-codegen-2.0.4.xsd">   <jdbc>     <driver>oracle.jdbc.driver.OracleDriver</driver>     <url>jdbc:oracle:thin:@dbhost:1700:DBSID</url>     <user>DB_FTRS</user>     <password>password</password>   </jdbc>   <generator>     <name>org.jooq.util.DefaultGenerator</name>     <database>       <name>org.jooq.util.oracle.OracleDatabase</name>     ...

Using Rapids cuDF in a Colab notebook

During last Spark+AI Summit Europe 2019 I had a chance to attend a talk from Miguel Martinez  who was presenting Rapids , the new Open Source framework from NVIDIA for GPU accelerated end-to-end Data Science and Analytics. Fig. 1 - Overview of the Rapids eco-system Rapids is a suite of Open Source libraries: cuDF cuML cuGraph cuXFilter I enjoied the presentation and liked the idea of this initiative, so I wanted to start playing with the Rapids libraries in Python on Colab , starting from cuDF, but the first attempt came with an issue that I eventually solved. So in this post I am going to share how I fixed it, with the hope it would be useful to someone else running into the same blocker. I am assuming here you are already familiar with Google Colab. I am using Python 3.x as Python 2 isn't supported by Rapids. Once you have created a new notebook in Colab, you need to check if the runtime for it is set to use Python 3 and uses a GPU as hardware accelerator. You...