Decisions How are predictions used to make decisions that provide the proposed value to the end user? ML task Input, output to predict, type of problem. Value Propositions What are we trying to do for the end user(s) of the predictive system? What objectives are we serving? Data Sources Which raw data sources can we use (internal and external)?
This article is the 2nd in a series dedicated to Machine Learning platforms. It was supported by Digital Catapult and PAPIs. In the previous article, I presented an overview of ML development platforms, whose job is to help create and package ML models. Model building is just one capability, out of many, required in ML systems. I ended that article by mentioning other types of ML platforms, and limitations when building real-world ML systems.
Machine Learning systems are complex. At their core, they ingest data in a certain format, to build models that are able to predict the future. A famous example in the industry is identifying fragile customers, who may stop being customers within a certain number of days (the "churn" problem). These predictions only become valuable when they are used to inform or to automate decisions (e.g. which promotional offers to give to which customers, to make them stay). In many organizations, there is often a disconnect between the people who are able to build accurate predictive models, and those who know how to best serve the organization's objectives.
Conventional models for Visual Question Answering (VQA) explore deterministic approaches with various types of image features, question features, and attention mechanisms. However, there exist other modalities that can be explored in addition to image and question pairs to bring extra information to the models. In this work, we propose latent variable models for VQA where extra information (e.g. captions and answer categories) are incorporated as latent variables to improve inference, which in turn benefits question-answering performance. Experiments on the VQA v2.0 benchmarking dataset demonstrate the effectiveness of our proposed models in that they improve over strong baselines, especially those that do not rely on extensive language-vision pre-training.
Stacking or Stacked Generalization is an ensemble machine learning algorithm. It uses a meta-learning algorithm to learn how to best combine the predictions from two or more base machine learning algorithms. The benefit of stacking is that it can harness the capabilities of a range of well-performing models on a classification or regression task and make predictions that have better performance than any single model in the ensemble. In this tutorial, you will discover the stacked generalization ensemble or stacking in Python. Stacking Ensemble Machine Learning With Python Photo by lamoix, some rights reserved.