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  • Mlops

MLOps Courses

MLOps courses can help you learn deployment strategies, model monitoring, and continuous integration and delivery for machine learning projects. You can build skills in automating workflows, managing data pipelines, and ensuring compliance with governance standards. Many courses introduce tools like TensorFlow, Kubernetes, and MLflow, that support operationalizing AI models and streamlining collaboration between data scientists and IT teams.


Popular MLOps Courses and Certifications


  • D

    DeepLearning.AI

    Machine Learning in Production

    Skills you'll gain: Model Deployment, MLOps (Machine Learning Operations), Cloud Deployment, Continuous Deployment, Model Evaluation, Data Preprocessing, Machine Learning, Applied Machine Learning, Data Validation, Feature Engineering, Data Quality, Debugging, Continuous Monitoring, Data Pipelines

    4.8
    Rating, 4.8 out of 5 stars
    ·
    3.4K reviews

    Intermediate · Course · 1 - 4 Weeks

  • D

    Duke University

    MLOps | Machine Learning Operations

    Skills you'll gain: MLOps (Machine Learning Operations), Model Deployment, Cloud Deployment, Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Hugging Face, Responsible AI, Data Manipulation, Exploratory Data Analysis, Containerization, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis

    4.2
    Rating, 4.2 out of 5 stars
    ·
    596 reviews

    Advanced · Specialization · 3 - 6 Months

  • K

    KodeKloud

    Deploy ML Models to Production

    Skills you'll gain: AWS SageMaker, Model Deployment, MLOps (Machine Learning Operations), Data Governance, Data Security, Application Deployment, Personally Identifiable Information, Cloud Deployment, General Data Protection Regulation (GDPR), Continuous Deployment, Model Evaluation, Application Programming Interface (API), Continuous Monitoring

    Beginner · Course · 1 - 4 Weeks

  • G

    Google Cloud

    Machine Learning Operations (MLOps): Getting Started

    Skills you'll gain: MLOps (Machine Learning Operations), Model Deployment, Google Cloud Platform, Model Evaluation, DevOps, Cloud Deployment, Continuous Deployment, Machine Learning, Continuous Integration, Automation, Data Pipelines

    4
    Rating, 4 out of 5 stars
    ·
    481 reviews

    Intermediate · Course · 1 - 4 Weeks

  • P

    Pragmatic AI Labs

    Production Governance and MLOps on Databricks

    Skills you'll gain: Databricks, Role-Based Access Control (RBAC), MLOps (Machine Learning Operations), Data Lakes, Data Governance, GitHub, Model Deployment, Data Management, CI/CD, Data Quality, Git (Version Control System), Continuous Integration, Data Engineering, Continuous Monitoring, Python Programming, Command-Line Interface

    Intermediate · Course · 1 - 4 Weeks

  • Status: Trending in AI
    Trending in AI
    M

    Microsoft

    Microsoft AI & ML Engineering

    Skills you'll gain: Unsupervised Learning, Model Deployment, Generative AI, Large Language Modeling, Data Management, Natural Language Processing, MLOps (Machine Learning Operations), Supervised Learning, Microsoft Azure, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Generative Adversarial Networks (GANs), Infrastructure Architecture, LLM Application, Responsible AI, Generative AI Agents, Applied Machine Learning, Azure DevOps, Reinforcement Learning, Data Preprocessing

    4.5
    Rating, 4.5 out of 5 stars
    ·
    344 reviews

    Intermediate · Professional Certificate · 3 - 6 Months

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  • D

    Duke University

    DevOps, DataOps, MLOps

    Skills you'll gain: MLOps (Machine Learning Operations), Responsible AI, Artificial Intelligence and Machine Learning (AI/ML), PyTorch (Machine Learning Library), Containerization, Tensorflow, Rust (Programming Language), Microsoft Copilot, DevOps, Hugging Face, Applied Machine Learning, Cloud Solutions, CI/CD, Machine Learning, Serverless Computing, Docker (Software), GitHub, Big Data

    4.1
    Rating, 4.1 out of 5 stars
    ·
    217 reviews

    Advanced · Course · 1 - 3 Months

  • D

    Duke University

    Large Language Model Operations (LLMOps)

    Skills you'll gain: Prompt Engineering, Databricks, Large Language Modeling, Model Deployment, LLM Application, Generative AI, Performance Analysis, Retrieval-Augmented Generation, Generative Model Architectures, Apache Airflow, Workflow Management, Hugging Face, Amazon Bedrock, Vector Databases, Data Lakes, ChatGPT, Extract, Transform, Load, OpenAI, Multimodal Prompts, MLOps (Machine Learning Operations)

    4.4
    Rating, 4.4 out of 5 stars
    ·
    296 reviews

    Beginner · Specialization · 3 - 6 Months

  • P

    Packt

    AI Agents and MLOps for Production-Ready AI

    Skills you'll gain: MLOps (Machine Learning Operations), Containerization, AI Workflows, Model Deployment, Generative AI Agents, LangGraph, Artificial Intelligence and Machine Learning (AI/ML), CrewAI, BeeAI, Docker (Software), Agentic systems, Google Cloud Platform, AWS SageMaker, CI/CD, Cloud Platforms, DevOps, Azure DevOps Pipelines, Kubernetes, Applied Machine Learning, Scalability

    Intermediate · Course · 1 - 4 Weeks

  • A

    Amazon Web Services

    Developing Machine Learning Solutions

    Skills you'll gain: Model Evaluation, MLOps (Machine Learning Operations), AWS SageMaker, Amazon Web Services, AI Workflows, Model Deployment, Machine Learning, Applied Machine Learning

    4.5
    Rating, 4.5 out of 5 stars
    ·
    115 reviews

    Beginner · Course · 1 - 4 Weeks

  • W

    Whizlabs

    AWS: Machine Learning & MLOps Foundations

    Skills you'll gain: MLOps (Machine Learning Operations), Data Preprocessing, AWS SageMaker, Model Evaluation, Model Deployment, Artificial Intelligence and Machine Learning (AI/ML), Amazon Web Services, Predictive Modeling, Machine Learning, Supervised Learning, Data Transformation, Unsupervised Learning, Classification Algorithms

    4.3
    Rating, 4.3 out of 5 stars
    ·
    6 reviews

    Intermediate · Course · 1 - 4 Weeks

  • P

    Pearson

    Learn MLOps for Machine Learning

    Skills you'll gain: MLOps (Machine Learning Operations), Model Deployment, AWS SageMaker, CI/CD, Data Management, Cloud Deployment, Model Evaluation, Data Preprocessing, Machine Learning, Automation, Data Pipelines, Continuous Monitoring

    Intermediate · Course · 1 - 4 Weeks

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In summary, here are 10 of our most popular mlops courses

  • Machine Learning in Production: DeepLearning.AI
  • MLOps | Machine Learning Operations: Duke University
  • Deploy ML Models to Production: KodeKloud
  • Machine Learning Operations (MLOps): Getting Started: Google Cloud
  • Production Governance and MLOps on Databricks: Pragmatic AI Labs
  • Microsoft AI & ML Engineering: Microsoft
  • DevOps, DataOps, MLOps: Duke University
  • Large Language Model Operations (LLMOps): Duke University
  • AI Agents and MLOps for Production-Ready AI: Packt
  • Developing Machine Learning Solutions: Amazon Web Services

Frequently Asked Questions about Mlops

MLOps, or Machine Learning Operations, is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. It combines machine learning, DevOps, and data engineering to streamline the process of taking models from development to deployment. The importance of MLOps lies in its ability to enhance collaboration between data scientists and operations teams, ensuring that machine learning models are not only built but also effectively integrated into business processes. This leads to improved model performance, faster deployment times, and ultimately, better decision-making based on data-driven insights.‎

A variety of job opportunities exist in the field of MLOps. Positions such as MLOps Engineer, Machine Learning Engineer, Data Engineer, and AI Operations Manager are common. These roles typically involve responsibilities like model deployment, monitoring, and optimization, as well as collaboration with cross-functional teams to ensure that machine learning solutions align with business objectives. The demand for MLOps professionals is growing as organizations increasingly rely on machine learning to drive innovation and efficiency.‎

To succeed in MLOps, you should develop a blend of technical and soft skills. Key technical skills include proficiency in programming languages such as Python and R, familiarity with machine learning frameworks like TensorFlow and PyTorch, and experience with cloud platforms like AWS or Azure. Additionally, understanding DevOps practices, version control systems, and containerization technologies like Docker can be beneficial. Soft skills such as problem-solving, communication, and teamwork are also essential, as MLOps often requires collaboration across various teams.‎

There are several online courses available to help you learn MLOps. Notable options include the MLOps | Machine Learning Operations Specialization and the Machine Learning Operations (MLOps): Getting Started course. These courses cover foundational concepts and practical applications, equipping you with the skills needed to implement MLOps in real-world scenarios.‎

Yes. You can start learning MLOps on Coursera for free in two ways:

  1. Preview the first module of many MLOps courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in MLOps, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn MLOps effectively, start by building a solid foundation in machine learning concepts and practices. You can then explore specialized courses that focus on MLOps tools and techniques. Engage in hands-on projects to apply what you've learned, and consider collaborating with peers or joining online communities to share knowledge and experiences. Continuous learning through courses, workshops, and real-world applications will help you stay updated in this rapidly evolving field.‎

MLOps courses typically cover a range of topics, including the machine learning lifecycle, model deployment strategies, monitoring and maintenance of models, and the integration of machine learning into business processes. You may also learn about specific tools and platforms used in MLOps, such as MLflow, Kubernetes, and cloud services like AWS and Azure. These topics provide a comprehensive understanding of how to manage machine learning models effectively.‎

For training and upskilling employees in MLOps, courses like MLOps Platforms: Amazon SageMaker and Azure ML and AWS: Machine Learning & MLOps Foundations are excellent choices. These courses are designed to equip teams with the necessary skills to implement MLOps practices, fostering a culture of continuous improvement and innovation within the organization.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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