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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
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
Introducing Apache Airflow on AWS
Categories: Big Data, Cloud Computing, Containers Orchestration | Tags: PySpark, Learning and tutorial, Airflow, Oozie, Spark, AWS, Docker, Python
Apache Airflow offers a potential solution to the growing challenge of managing an increasingly complex landscape of data management tools, scripts and analytics processes. It is an open-source…
May 5, 2020
Spark Streaming part 1: build data pipelines with Spark Structured Streaming
Categories: Data Engineering, Learning | Tags: Apache Spark Streaming, Kafka, Spark, Big Data, Streaming
Spark Structured Streaming is a new engine introduced with Apache Spark 2 used for processing streaming data. It is built on top of the existing Spark SQL engine and the Spark DataFrame. The…
Apr 18, 2019
What's new in Apache Spark 2.3?
Categories: Data Engineering, DataWorks Summit 2018 | Tags: Arrow, PySpark, Tuning, ORC, Spark, Spark MLlib, Data Science, Docker, Kubernetes, pandas, Streaming
Let’s dive into the new features offered by the 2.3 distribution of Apache Spark. This article is a composition of the following talks seen at the DataWorks Summit 2018 and additional research: Apache…
May 23, 2018