Collaborating Authors

Building Machine Learning Pipelines: Common Pitfalls -


In recent years, there have been rapid advancements in Machine Learning and this has led to many companies and startups delving into the field without understanding the pitfalls. Common examples are the pitfalls involved when building ML pipelines. Machine Learning pipelines are complex and there are several ways they can fail or be misused. Stakeholders involved in ML projects need to understand how Machine Learning pipelines can fail, possible pitfalls, and how to avoid such pitfalls. There are several pitfalls you should be aware of when building machine learning pipelines. The most common pitfall is the black-box problem -- where the pipeline is too complex to understand.

Execute Azure Machine Learning service pipelines in Azure Data Factory pipelines Azure updates Microsoft Azure


You now have the ability to run your Azure Machine Learning service pipelines as a step in your Azure Data Factory pipelines. This allows you to run your machine learning models with data from multiple sources (more than 85 data connectors supported in Data Factory). The seamless integration enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns. Get started quickly by creating an AzureMLService connection and AzureMLExecutePipelne activity to invoke your Azure Machine Learning pipelines in a Data Factory data pipeline.

Multiple pipelines that merge within a sklearn Pipeline?


As long as "Entire Data Set" means the same features, this is exactly what FeatureUnion does: If you have two different sets of features that you want to combine, you first need to put them into a single dataset, and then have each branch of the FeatureUnion first select the features it should operate on.

The Data Engineering Pipeline


Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Data Engineers are at the heart of the engine room of any data-driven company.

AVATAR -- Machine Learning Pipeline Evaluation Using Surrogate Model Machine Learning

The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution.