MLOps (Machine Learning Operations)

MLOps (Machine Learning Operations) is an engineering discipline that aims to unify machine learning system development and machine learning system operations. Coursera's MLOps catalogue teaches you how to streamline and regulate the process of deploying, testing, and improving machine learning models in production. You'll learn about essential elements of MLOps such as data and model versioning, model testing, monitoring, and validation, as well as robust strategies for deploying and maintaining ML models. By the end of your learning journey, you will be able to effectively manage the ML lifecycle, understand the role of automation in MLOps, and leverage best practices to bring data science and IT operations together.

Filter by

Subject
Required

Language
Required

The language used throughout the course, in both instruction and assessments.

Learning Product
Required

Build job-relevant skills in under 2 hours with hands-on tutorials.
Learn from top instructors with graded assignments, videos, and discussion forums.
Learn a new tool or skill in an interactive, hands-on environment.
Get in-depth knowledge of a subject by completing a series of courses and projects.
Earn career credentials from industry leaders that demonstrate your expertise.
Earn your Bachelor’s or Master’s degree online for a fraction of the cost of in-person learning.

Level
Required

Duration
Required

Subtitles
Required

Educator
Required

Hands-on Learning
Required

Tools
Required

Results for "MLOps (Machine Learning Operations)"

  • Skills you'll gain: Fine-tuning, MLOps (Machine Learning Operations), Model Deployment, Cloud Deployment, Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Hugging Face, GitHub Copilot, Unit Testing, Data Engineering, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis

  • Skills you'll gain: Responsible AI, MLOps (Machine Learning Operations), Data Preprocessing, Model Deployment, Data Ethics, Artificial Intelligence and Machine Learning (AI/ML), Apache Mahout, AI Security, Applied Machine Learning, Classification Algorithms, CI/CD, Java, Java Programming, Machine Learning Software, Jenkins, Deep Learning, Machine Learning, Spring Boot, Natural Language Processing, Reinforcement Learning

  • Skills you'll gain: Anomaly Detection, Feature Engineering, Fraud detection, Unsupervised Learning, Continuous Monitoring, Autoencoders, MLOps (Machine Learning Operations), Machine Learning Methods, Statistical Machine Learning, Model Training, Time Series Analysis and Forecasting, System Monitoring, Applied Machine Learning, Model Deployment, Statistical Analysis, Taxonomy

  • Skills you'll gain: Microsoft Azure, MLOps (Machine Learning Operations), Data Science, Machine Learning, Prompt Engineering Tools, Model Evaluation, Applied Machine Learning, Data Store, AI Workflows, Apache Spark, Data Strategy, Data Import/Export, Azure Synapse Analytics, Cloud Computing, Fine-tuning, Data Pipelines, Continuous Monitoring, Data Preprocessing, Scalability, Development Environment

  • Skills you'll gain: Model Evaluation, Data Preprocessing, JUnit, Model Training, Build Tools, MLOps (Machine Learning Operations), Java, Performance Tuning, Decision Tree Learning, Classification And Regression Tree (CART), Apache Maven, Data Structures, Random Forest Algorithm, Gradle, Data Pipelines, Software Architecture, Software Design, Object Oriented Programming (OOP), Apache, Machine Learning

  • Skills you'll gain: Model Deployment, MLOps (Machine Learning Operations), Data Preprocessing, Classification And Regression Tree (CART), Exploratory Data Analysis, Logistic Regression, Statistical Machine Learning, Model Evaluation, Model Training, Supervised Learning, Decision Tree Learning, Probability & Statistics, Data Processing, Statistical Software, Machine Learning Methods, Machine Learning Software, Workflow Management, Machine Learning, Correlation Analysis, Applied Machine Learning

  • Skills you'll gain: MLOps (Machine Learning Operations), Model Deployment, CI/CD, Continuous Deployment, Docker (Software), Kubernetes, Model Training, Containerization, AI Workflows, Model Optimization, Scalability, Devops Tools, DevOps, Continuous Monitoring

  • Skills you'll gain: AWS SageMaker, MLOps (Machine Learning Operations), Microsoft Azure, Model Deployment, Data Engineering, Exploratory Data Analysis, Data Pipelines, Model Training, Amazon Web Services, Feature Engineering, Cloud Solutions, Artificial Intelligence and Machine Learning (AI/ML), Model Evaluation, Data Preprocessing, Cloud Deployment, Data Analysis, Applied Machine Learning, Machine Learning Methods, Machine Learning, Python Programming

  • Skills you'll gain: Feature Engineering, Databricks, Data Engineering, PySpark, Data Lakes, Apache Airflow, Apache Spark, Data Pipelines, MLOps (Machine Learning Operations), Data Architecture, Data Processing, Artificial Intelligence and Machine Learning (AI/ML), Data Management, Data Storage, Python Programming, Artificial Intelligence, Machine Learning, SQL, Machine Learning Algorithms, Warehouse Management

  • Skills you'll gain: MLOps (Machine Learning Operations), Model Deployment, Data Modeling, Google Cloud Platform, Feature Engineering, Model Training, Data Store, Data Preprocessing, Data Processing, Data Management, Data Storage Technologies

  • Skills you'll gain: Pandas (Python Package), MLOps (Machine Learning Operations), NumPy, Unit Testing, Model Deployment, Data Manipulation, Test Script Development, Software Testing, Data Import/Export, Applied Machine Learning, Test Automation, Data Wrangling, Python Programming, Code Reusability, Data Processing, Debugging, Data Structures, Machine Learning, Object Oriented Programming (OOP), Scripting

  • Skills you'll gain: Responsible AI, Microsoft Azure, Unsupervised Learning, Databricks, MLOps (Machine Learning Operations), Applied Machine Learning, Classification Algorithms, Model Training, Regression Analysis, Scikit Learn (Machine Learning Library), Predictive Modeling, Model Deployment, Cloud Management, Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Model Evaluation, Supervised Learning, Machine Learning Algorithms, Data Pipelines, Data Transformation