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.
Key Takeaways
- Data Scientists build, train, and validate models to drive critical business decisions.
- Data Scientists are concerned with data access, reproducibility, and collaboration in order to make models quickly at scale.
- Adaltas enable Data Scientists to easily build, scale, and deploy machine learning models in minutes, helping drive innovation across the business.
Articles related to Data Science
Spring 2022 internship - building a Datalab
Categories: Data Science, Learning | Tags: Elasticsearch, MongoDB, Spark, Argo CD, Kubernetes, OpenID Connect, PostgreSQL
Job Description Over the last few years, we developed the ability to use computers to process large amount of data. The ecosystem evolved over a large offering of tools and libraries and the creation…
By David WORMS
Nov 24, 2021
H2O in practice: a protocol combining AutoML with traditional modeling approaches
Categories: Data Science, Learning | Tags: Automation, Cloud, H2O, Machine Learning, MLOps, On-premises, Open source, Python, XGBoost
H20 comes with a lot of functionalities. The second part of the series H2O in practice proposes a protocol to combine AutoML modeling with traditional modeling and optimization approach. The objective…
Nov 12, 2021
H2O in practice: a Data Scientist feedback
Categories: Data Science, Learning | Tags: Automation, Cloud, H2O, Machine Learning, MLOps, On-premises, Open source, Python
Automated machine learning (AutoML) platforms are gaining popularity and becoming a new important tool in the data scientists’ toolbox. A few months ago, I introduced H2O, an open-source platform for…
Sep 29, 2021
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…
Mar 31, 2021
Storage size and generation time in popular file formats
Categories: Data Engineering, Data Science | Tags: Avro, HDFS, Hive, ORC, Big Data, JSON, Parquet, Data Lake, File Format
Choosing an appropriate file format is essential, whether your data transits on the wire or is stored at rest. Each file format comes with its own advantages and disadvantages. We covered them in a…
Mar 22, 2021
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…
Mar 5, 2021
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…
Dec 10, 2020
Data versioning and reproducible ML with DVC and MLflow
Categories: Data Science, DevOps & SRE, Events | Tags: Data Engineering, Git, Databricks, Delta Lake, Machine Learning, MLflow, Storage
Our talk on data versioning and reproducible Machine Learning proposed to the Data + AI Summit (formerly known as Spark+AI) is accepted. The summit will take place online the 17-19th November…
Sep 30, 2020
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
Introduction to Databricks Community Edition and MLflow Every day the number of tools helping Data Scientists to build models faster increases. Consequently, the need to manage the results and the…
Sep 10, 2020
Version your datasets with Data Version Control (DVC) and Git
Categories: Data Science, DevOps & SRE | Tags: DevOps, Git, Infrastructure, Operation, GitOps, SCM
Using a Version Control System such as Git for source code is a good practice and an industry standard. Considering that projects focus more and more on data, shouldn’t we have a similar approach such…
By Grégor JOUET
Sep 3, 2020
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
During a Machine Learning project we need to keep track of the training data we are using. This is important for audit purposes and for assessing the performance of the models, developed at a later…
May 21, 2020
MLflow tutorial: an open source Machine Learning (ML) platform
Categories: Data Engineering, Data Science, Learning | Tags: Deep Learning, AWS, Azure, Databricks, Deployment, Machine Learning, MLflow, MLOps, Python, Scikit-learn
Introduction and principles of MLflow With increasingly cheaper computing power and storage and at the same time increasing data collection in all walks of life, many companies integrated Data Science…
Mar 23, 2020
Introduction to Ludwig and how to deploy a Deep Learning model via Flask
Categories: Data Science, Tech Radar | Tags: Deep Learning, Learning and tutorial, Ludwig Deep Learning Toolbox, Machine Learning, Python
Over the past decade, Machine Learning and deep learning models have proven to be very effective in performing a wide variety of tasks such as fraud detection, product recommendation, autonomous…
Mar 2, 2020
Internship Data Science & Data Engineer - ML in production and streaming data ingestion
Categories: Data Engineering, Data Science | Tags: Flink, DevOps, Hadoop, HBase, Kafka, Spark, Kubernetes, 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, Horovod, Keras, TensorFlow
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: DevOps, Operation, AI, Cloud, Machine Learning, MLOps, On-premises, 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: CPU, Deep Learning, Jupyter, Linux, AI, Docker, 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: Apache Spark Streaming, Big Data, Spark, 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…
Jun 27, 2019
Introduction to Cloudera Data Science Workbench
Categories: Data Science | Tags: Cloudera, Git, Azure, Docker, Kubernetes, Machine Learning, 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, 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
CodaLab – Data Science competitions
Categories: Data Science, Adaltas Summit 2018, Learning | Tags: Database, Infrastructure, MySQL, Machine Learning, 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: Caffe, Deep Learning, Edge computing, GPU, NVIDIA, AI, Keras, 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, Micro Services, Open API, Kubernetes, Neural Network, Node.js
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: YARN, Deep Learning, GPU, Hadoop, Spark, Spark MLlib, PyTorch, TensorFlow, XGBoost, MXNet
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, YARN, C++, CPU, GPU, JavaScript, Tuning, Spark, Keras, Kubernetes, Machine Learning, Python, TensorFlow
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, Tools, Hadoop, Machine Learning
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, Storm, Tools, Hadoop, Kafka, Data Science, Machine Learning
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, Hadoop, Mahout, Clustering, Machine Learning
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