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_implementing-automl-techniques-at-salesforce-scale-matthew-tovbin-salesforce.webp "Building efficient machine learning applications is not a simple task. The typical engineering process is an iteration of data wrangling, feature generation, model selection, hyperparameter tuning and evaluation. The amount of possible variations of input features, algorithms and parameters makes it too complex to perform efficiently even by experts. Automating this process is especially important when building machine learning applications for thousands of customers.nnIn this talk I demonstrate")
⏲ 17 min 7 sec ✓ 11-Jun-2018
_how-to-evaluate-ml-models-124-evaluation-metrics-for-machine-learning.webp "AssemblyAI")
_machine-learning-evaluation.webp "Computational Thinking")
_splitting-amp-categorical-casting-124-intro-to-azure-ml-part-9.webp "Before we can feed this dataset into a machine learning model there are two things we have to take care of. First we have to make sure all the categorical features are treated as categories. We’ll use the editmeta data module once again to cast these features. Then we need to setup a holdout dataset for future evaluation of any model that we build. We will randomly sample our dataset into two partitions, a training set and a test set. The test set we will lock away to pretend that its future w")
_evaluation-metrics-for-machine-learning-models-124-full-course.webp "Analytics Vidhya")
_6-9-model-evaluation-in-machine-learning-124-accuracy-score-124-mean-squared-error.webp "Siddhardhan")
⏲ 15 minutes 48 seconds 👁 5K
_operationalizing-machine-learningmanaging-provenance-from-raw-data-to-predictions-nabeel-sarwar-comcast-nbc-universal.webp "Our team at Comcast is challenged with operationalizing predictive ML models to improve customer experience. Our goal is to eliminate bottlenecks in the process from model inception to deployment and monitoring.nnTraditionally CI/CD manages code and infrastructure artifacts like container definitions. We want to extend it to support granular traceability enabling tracking of ML Models from use-case, to feature/attribute selection, development of versioned datasets, model training code, model eva")
⏲ 17 min 88 sec ✓ 11-Jun-2018
_all-machine-learning-models-explained-in-5-minutes-124-types-of-ml-models-basics.webp "Learn with Whiteboard")
⏲ 5 minutes 1 second 👁 587.7K
_105-evaluating-a-classification-model-6-classification-report-124-creating-machine-learning-models.webp "Machine Learning")
⏲ 10 minutes 17 seconds 👁 19.5K
_model-parallelism-in-spark-ml-cross-validation-nick-pentreath-ibm-and-bryan-cutler-ibm.webp "Tuning a Spark ML model with cross-validation can be an extremely computationally expensive process. As the number of hyperparameter combinations increases, so does the number of models being evaluated. The default configuration in Spark is to evaluate each of these models one-by-one to select the best performing. When running this process with a large number of models, if the training and evaluation of a model does not fully utilize the available cluster resources then that waste will be compou")
⏲ 19 min 11 sec ✓ 11-Jun-2018
_maria-khalusova-machine-learning-model-evaluation-metrics-124-pydata-la-2019.webp "PyData")
⏲ 39 minutes 47 seconds 👁 4.4K
_8-8-precision-recall-f1-score-124-model-evaluation.webp "Siddhardhan")
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