Machine Learning Operations

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Deploying Machine Learning Models in Production (Coursera)

Oct 12th 2022
Deploying Machine Learning Models in Production (Coursera)
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In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will [...]
Oct 12th 2022
Course Auditing
42.00 EUR/month

MLOps (Machine Learning Operations) Fundamentals (Coursera)

Oct 10th 2022
MLOps (Machine Learning Operations) Fundamentals (Coursera)
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and [...]

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 [...]

Machine Learning Operations 2 (MLOps2-AML): Data Pipeline Automation & Optimization 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 to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course, MLOps2: Data [...]

Machine Learning Operations 2 (MLOps2-GCP): Data Pipeline Automation & Optimization 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 to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course, MLOp2s: Data [...]

Machine Learning Operations 2 (MLOps2-AWS): Data Pipeline Automation & Optimization 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 to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course - MLOp2s: [...]

MLOps for Scaling TinyML (edX)

This course introduces learners to Machine Learning Operations (MLOps) through the lens of TinyML (Tiny Machine Learning). Learners explore best practices to deploy, monitor, and maintain (tiny) Machine Learning models in production at scale.