Data Science
Data Science, and more generally Artificial Intelligence (AI), differs from traditional programming and analysis in its ability to extract knowledge from data and modify its behavior and learn without specific programming. While traditional software predefines the logic that governs their processes, Data Science's algorithms build and discover models and are able to continually improve them.
Data Science brings together a set of skills including Machine Learning, Natural Language Processing (NLP), speech, images and faces recognition (among other applications). In some applications, the algorithms go so far as to simulate human intelligence.
Articles related to Data Science
Internship Data Science & Data Engineer - ML in production and streaming data ingestion
Categories: Data Engineering, Data Science | Tags: Flink, Kafka, Spark, DevOps, Kubernetes, Hadoop, HBase, Python
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…
By David WORMS
Nov 26, 2019
Avoid Bottlenecks in distributed Deep Learning pipelines with Horovod
Categories: Data Science | Tags: Deep Learning, GPU, Keras, TensorFlow, Horovod
The Deep Learning training process can be greatly speed up using a cluster of GPUs. When dealing with huge amounts of data, distributed computing quickly becomes a challenge. A common obstacle which…
By Grégor JOUET
Nov 15, 2019
Innovation, project vs product culture in Data Science
Categories: Data Science, Data Governance | Tags: DevOps, Agile, Scrum
Data Science carries the jobs of tomorrow. It is closely linked to the understanding of the business usecases, the behaviors and the insights that will be extracted from existing data. The stakes are…
By David WORMS
Oct 8, 2019
Machine Learning model deployment
Categories: Big Data, Data Engineering, Data Science, DevOps & SRE | Tags: AI, Cloud, DevOps, Machine Learning, On-premise, Operation, Schema
“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…
Sep 30, 2019
TensorFlow installation on Docker
Categories: Containers Orchestration, Data Science, Learning | Tags: AI, CPU, Deep Learning, Docker, Jupyter, Linux, TensorFlow
TensorFlow is an Open Source software from Google for numerical computation using a graph representation: Vertex (nodes) represent mathematical operations Edges represent N-dimensional data array…
Aug 5, 2019
Spark Streaming part 4: clustering with Spark MLlib
Categories: Data Engineering, Data Science, Learning | Tags: Spark, Apache Spark Streaming, Big Data, Clustering, Machine Learning, Scala, Streaming
Spark MLlib is an Apache’s Spark library offering scalable implementations of various supervised and unsupervised Machine Learning algorithms. Thus, Spark framework can serve as a platform for…
Jul 11, 2019
Introduction to Cloudera Data Science Workbench
Categories: Data Science | Tags: Cloudera, Docker, Git, Kubernetes, Machine Learning, Azure, Notebook
Cloudera Data Science Workbench is a platform that allows Data Scientists to create, manage, run and schedule data science workflows from their browser. Thus it enables them to focus on their main…
Feb 28, 2019
Applying Deep Reinforcement Learning to Poker
Categories: Data Science | Tags: Algorithm, Deep Learning, Gaming, Machine Learning, Q-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
CodaLab – Data Science competitions
Categories: Data Science, Adaltas Summit 2018, Learning | Tags: Database, Infrastructure, Machine Learning, MySQL, Node.js, Python
CodaLab Competition is a platform for code execution in the field of Data Science. It is a web interface on which a user can submit code or results and compare themselves to others. Let’s see how it…
Dec 17, 2018
Nvidia and AI on the edge
Categories: Data Science | Tags: AI, Caffe, Deep Learning, Edge computing, GPU, Keras, NVIDIA, PyTorch, TensorFlow
In the last four years, corporations have been investing a lot in AI and particularly in Deep Learning and Edge Computing. While the theory has taken huge steps forward and new algorithms are invented…
By Yliess HATI
Oct 10, 2018
Lando: Deep Learning used to summarize conversations
Categories: Data Science, Learning | Tags: Deep Learning, Kubernetes, Micro Services, Node.js, Open API, Neural Network
Lando is an application to summarize conversations using Speech To Text to translate the written record of a meeting into text and Deep Learning technics to summarize contents. It allows users to…
By Yliess HATI
Sep 18, 2018
Deep learning on YARN: running Tensorflow and friends on Hadoop cluster
Categories: Data Science | Tags: Spark, Spark MLlib, YARN, Deep Learning, GPU, PyTorch, TensorFlow, XGBoost, Hadoop
With the arrival of Hadoop 3, YARN offer more flexibility in resource management. It is now possible to perform Deep Learning analysis on GPUs with specific development environments, leveraging…
Jul 24, 2018
YARN and GPU Distribution for Machine Learning
Categories: Data Science, DataWorks Summit 2018 | Tags: YARN, GPU, Machine Learning, Neural Network, Storage
This article goes over the fundamental principles of Machine Learning and what tools are currently used to run machine learning algorithms. We will then see how a resource manager such as YARN can be…
By Grégor JOUET
May 30, 2018
TensorFlow on Spark 2.3: The Best of Both Worlds
Categories: Data Science, DataWorks Summit 2018 | Tags: Mesos, Spark, YARN, C++, CPU, GPU, JavaScript, Keras, Kubernetes, Machine Learning, TensorFlow, Tuning, Python
The integration of TensorFlow With Spark has a lot of potential and creates new opportunities. This article is based on a conference seen at the DataWorks Summit 2018 in Berlin. It was about the new…
By Yliess HATI
May 29, 2018
Apache Apex with Apache SAMOA
Categories: Data Science, Events, Tech Radar | Tags: Apex, Flink, Samoa, Storm, Machine Learning, Tools, Hadoop
Traditional Machine Learning Batch Oriented Supervised - most common Training and Scoring One time model building Data set Training: Model building Holdout: Paremeter tuning Test: Accuracy Online…
Jul 17, 2016
Apache Apex: next gen Big Data analytics
Categories: Data Science, Events, Tech Radar | Tags: Apex, Flink, Kafka, Storm, Data Science, Machine Learning, Tools, Hadoop
Below is a compilation of my notes taken during the presentation of Apache Apex by Thomas Weise from DataTorrent, the company behind Apex. Introduction Apache Apex is an in-memory distributed parallel…
Jul 17, 2016
Definitions of machine learning algorithms present in Apache Mahout
Categories: Data Science | Tags: Algorithm, Сlassification, Clustering, Machine Learning, Hadoop, Mahout
Apache Mahout is a machine learning library built for scalability. Its core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop…
By David WORMS
Mar 8, 2013
Hadoop and R with RHadoop
Categories: Business Intelligence, Data Science | Tags: HDFS, MapReduce, Thrift, Data Analytics, Learning and tutorial, R, Hadoop, HBase
RHadoop is a bridge between R, a language and environment to statistically explore data sets, and Hadoop, a framework that allows for the distributed processing of large data sets across clusters of…
By David WORMS
Jul 19, 2012
Installing and using MADlib with PostgreSQL on OSX
Categories: Data Science | Tags: Database, Greenplum, Statistics, PostgreSQL, SQL
We cover basic installation and usage of PostgreSQL and MADlib on OSX and Ubuntu. Instructions for other environments should be similar. PostgreSQL is an Open Source database with enterprise…
By David WORMS
Jul 7, 2012