Machine Learning Pipelines

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AI Capstone Project with Deep Learning (Coursera)

May 30th 2022
AI Capstone Project with Deep Learning (Coursera)
Course Auditing
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In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate [...]

Machine Learning Operations 1 (MLOps1-GCP): Deploying AI & ML Models in Production using Google Cloud Platform (GCP) (edX)

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we [...]

Machine Learning Operations 1 (MLOps1-AML): Deploying AI & ML Models in Production using Microsoft Azure Machine Learning (AML) (edX)

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we [...]

Machine Learning Operations 1 (MLOps1-AWS): Deploying AI & ML Models in Production using Amazon Web Services (AWS) (edX)

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we [...]

Distributed Machine Learning with Apache Spark (edX)

Learn the underlying principles required to develop scalable machine learning pipelines and gain hands-on experience using Apache Spark. Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability and optimization.