Python
Python is a dynamic, interpretive and scripted programming language. It was developed at the beginning of the 1990s by Guido van Rossum. Today, this language is being developed as an open-source project by many developers worldwide, led by Guido through the Python Software Foundation (PSF). The goal of the language is to develop an easy-to-learn programming language producing an intuitive and readable code while remaining as powerful as other established programming languages.
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Modern Python Environment 1: start a project with pyenv & poetry
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When learning a programming language, the focus is essentially on understanding the syntax, the code style, and the underlying concepts. With time, you become sufficiently comfortable with the…
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Apache Liminal: when MLOps meets GitOps
Categories: Big Data, Containers Orchestration, Data Engineering, Data Science, Tech Radar | Tags: Data Engineering, Deep Learning, CI/CD, Data Science, Deployment, Docker, GitOps, Kubernetes, Machine Learning, MLOps, Open source, Python, TensorFlow
Apache Liminal is an open-source software which proposes a solution to deploy end-to-end Machine Learning pipelines. Indeed it permits to centralize all the steps needed to construct Machine Learning…
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TensorFlow Extended (TFX): the components and their functionalities
Categories: Big Data, Data Engineering, Data Science, Learning | Tags: Beam, Data Engineering, Deep Learning, Pipeline, CI/CD, Data Science, Deployment, Machine Learning, MLOps, Open source, Python, TensorFlow
Putting Machine Learning (ML) and Deep Learning (DL) models in production certainly is a difficult task. It has been recognized as more failure-prone and time consuming than the modeling itself, yet…
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Faster model development with H2O AutoML and Flow
Categories: Data Science, Learning | Tags: Automation, Cloud, H2O, Machine Learning, MLOps, On-premises, Open source, Python
Building Machine Learning (ML) models is a time-consuming process. It requires expertise in statistics, ML algorithms, and programming. On top of that, it also requires the ability to translate a…
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Experiment tracking with MLflow on Databricks Community Edition
Categories: Data Engineering, Data Science, Learning | Tags: Deep Learning, Spark, Databricks, Delta Lake, Machine Learning, MLflow, Notebook, Python, Scikit-learn
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Importing data to Databricks: external tables and Delta Lake
Categories: Data Engineering, Data Science, Learning | Tags: Parquet, AWS, Amazon S3, Azure Data Lake Storage (ADLS), Databricks, Delta Lake, Python
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Introducing Apache Airflow on AWS
Categories: Big Data, Cloud Computing, Containers Orchestration | Tags: Airflow, Oozie, PySpark, Learning and tutorial, Spark, AWS, Docker, Python
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Optimisation of Spark applications in Hadoop YARN
Categories: Data Engineering, Learning | Tags: Tuning, Hadoop, Spark, Python
Apache Spark is an in-memory data processing tool widely used in companies to deal with Big Data issues. Running a Spark application in production requires user-defined resources. This article…
Mar 30, 2020
MLflow tutorial: an open source Machine Learning (ML) platform
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Mar 23, 2020
Introduction to Ludwig and how to deploy a Deep Learning model via Flask
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Internship Data Science & Data Engineer - ML in production and streaming data ingestion
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Context The exponential evolution of data has turned the industry upside down by redefining data storage, processing and data ingestion pipelines. Mastering these methods considerably facilitates…
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Machine Learning model deployment
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“Enterprise Machine Learning requires looking at the big picture […] from a data engineering and a data platform perspective,” lectured Justin Norman during the talk on the deployment of Machine…
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TensorFlow installation on Docker
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Running Apache Hive 3, new features and tips and tricks
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Jul 25, 2019
Auto-scaling Druid with Kubernetes
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Apache Druid is an open-source analytics data store which could leverage the auto-scaling abilities of Kubernetes due to its distributed nature and its reliance on memory. I was inspired by the talk…
Jul 16, 2019
Spark Streaming part 3: DevOps, tools and tests for Spark applications
Categories: Big Data, Data Engineering, DevOps & SRE | Tags: Apache Spark Streaming, DevOps, Learning and tutorial, Spark
Whenever services are unavailable, businesses experience large financial losses. Spark Streaming applications can break, like any other software application. A streaming application operates on data…
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Spark Streaming part 2: run Spark Structured Streaming pipelines in Hadoop
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First Class Functions in Python
Categories: Hack, Learning | Tags: Programming, Python
I recently watched a talk by Dave Cheney about first class functions in Go. Python supports first class functions too, so can we use them in the same ways? Absolutely. I have been using Python for a…
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Publish Spark SQL DataFrame and RDD with Spark Thrift Server
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Applying Deep Reinforcement Learning to Poker
Categories: Data Science | Tags: Algorithm, Deep Learning, Gaming, Q-learning, Machine Learning, Neural Network, Python
We will cover the subject of Deep Reinforcement Learning, more specifically the Deep Q Learning algorithm introduced by DeepMind, and then we’ll apply a version of this algorithm to the game of Poker…
Jan 9, 2019
Monitoring a production Hadoop cluster with Kubernetes
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Monitoring a production grade Hadoop cluster is a real challenge and needs to be constantly evolving. The software we use today is based on Nagios. Very efficient when it comes to the simplest…
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CodaLab – Data Science competitions
Categories: Data Science, Adaltas Summit 2018, Learning | Tags: Database, Infrastructure, MySQL, Machine Learning, Node.js, Python
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Present and future of Hadoop workflow scheduling: Oozie 5.x
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What's new in Apache Spark 2.3?
Categories: Data Engineering, DataWorks Summit 2018 | Tags: Arrow, ORC, PySpark, Streaming, Tuning, Spark, Spark MLlib, Data Science, Docker, Kubernetes, pandas
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Categories: Containers Orchestration, DevOps & SRE, Open Source Summit Europe 2017 | Tags: Ansible, Docker Compose, pip, Shell, YAML, Docker
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Oct 16, 2017
HDP cluster monitoring
Categories: Big Data, DevOps & SRE, Infrastructure | Tags: Alert, Ambari, HDP, Metrics, Monitoring, REST
With the current growth of BigData technologies, more and more companies are building their own clusters in hope to get some value of their data. One main concern while building these infrastructures…
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Get in control of your workflows with Apache Airflow
Categories: Big Data, Tech Radar | Tags: Airflow, DevOps, Cloud, Python
Below is a compilation of my notes taken during the presentation of Apache Airflow by Christian Trebing from BlueYonder. Introduction Use case: how to handle data coming in regularly from customers…
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Splitting HDFS files into multiple hive tables
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I am going to show how to split a CSV file stored inside HDFS as multiple Hive tables based on the content of each record. The context is simple. We are using Flume to collect logs from all over our…
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