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    • Mlops

    MLOps Courses Online

    Master MLOps for managing machine learning models in production. Learn about deployment, monitoring, and lifecycle management of ML models.

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    Explore the MLOps Course Catalog

    • CertNexus

      Finalize a Data Science Project

      Skills you'll gain: Applied Machine Learning, Machine Learning, Stakeholder Engagement, Data Presentation, Stakeholder Communications, MLOps (Machine Learning Operations), Web Applications, Project Closure, Data-Driven Decision-Making, Solution Delivery, Data Pipelines, Automation

      Intermediate · Course · 1 - 4 Weeks

    • Fractal Analytics

      Introduction to Vertex AI

      Skills you'll gain: MLOps (Machine Learning Operations), Generative AI, Artificial Intelligence and Machine Learning (AI/ML), Google Cloud Platform, Technology Strategies, Machine Learning, User Interface (UI), Cloud Computing

      4.9
      Rating, 4.9 out of 5 stars
      ·
      8 reviews

      Beginner · Course · 1 - 4 Weeks

    • Google Cloud

      Smart Analytics, Machine Learning, and AI on GCP en Español

      Skills you'll gain: Google Cloud Platform, Unstructured Data, MLOps (Machine Learning Operations), Tensorflow, Big Data, Data Pipelines, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Natural Language Processing

      4.8
      Rating, 4.8 out of 5 stars
      ·
      44 reviews

      Intermediate · Course · 1 - 3 Months

    • CertNexus

      Follow a Machine Learning Workflow

      Skills you'll gain: Data Analysis, Data Collection, Workflow Management, MLOps (Machine Learning Operations), Statistical Analysis, Artificial Intelligence and Machine Learning (AI/ML), Exploratory Data Analysis, Applied Machine Learning, Machine Learning, Feature Engineering, Data Modeling, Application Deployment

      4.8
      Rating, 4.8 out of 5 stars
      ·
      18 reviews

      Intermediate · Course · 1 - 3 Months

    • Duke University

      Beginning Llamafile for Local Large Language Models (LLMs)

      Skills you'll gain: Large Language Modeling, Application Programming Interface (API), Restful API, Natural Language Processing, MLOps (Machine Learning Operations), JSON, Servers, Command-Line Interface, Open Source Technology

      Beginner · Course · 1 - 4 Weeks

    • Google Cloud

      Machine Learning with TensorFlow on Google Cloud en Français

      Skills you'll gain: Feature Engineering, Exploratory Data Analysis, Tensorflow, Data Analysis, Keras (Neural Network Library), Google Cloud Platform, Applied Machine Learning, Data Quality, Data Cleansing, Data Strategy, Supervised Learning, Machine Learning, Performance Tuning, Artificial Intelligence and Machine Learning (AI/ML), MLOps (Machine Learning Operations), Dataflow, Deep Learning, Machine Learning Algorithms, Data Processing, Artificial Neural Networks

      4.2
      Rating, 4.2 out of 5 stars
      ·
      30 reviews

      Intermediate · Specialization · 3 - 6 Months

    • H2O.ai

      Large Language Models

      Skills you'll gain: Large Language Modeling, Collaborative Software, Artificial Neural Networks, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Generative AI, Prompt Engineering, Machine Learning Methods, Training Programs, Data Validation, Data Cleansing, Program Evaluation, Business Logic, Natural Language Processing, Applied Machine Learning, AI Personalization, MLOps (Machine Learning Operations), Data Processing, Data Transformation, Verification And Validation

      3.8
      Rating, 3.8 out of 5 stars
      ·
      10 reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free
      Free

      University of Colorado Boulder

      컴퓨터 비전 분야에서의 딥 러닝 응용 사례

      Skills you'll gain: Image Analysis, Tensorflow, Computer Vision, Deep Learning, Machine Learning, Artificial Neural Networks, Performance Tuning, Artificial Intelligence and Machine Learning (AI/ML), Computer Graphics

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free

      Duke University

      Foundations of Local Large Language models

      Skills you'll gain: Generative AI, Cloud Applications, Application Deployment, Large Language Modeling, Other Programming Languages, Data Ethics, MLOps (Machine Learning Operations), Prompt Engineering, Statistical Programming, Risk Management Framework, Natural Language Processing, Performance Testing, Command-Line Interface, Rust (Programming Language)

      4.3
      Rating, 4.3 out of 5 stars
      ·
      16 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free
      Free

      University of Pennsylvania

      로봇공학: 캡스톤 디자인

      Skills you'll gain: Control Systems, Automation, Artificial Intelligence, Matlab, Embedded Software, Simulations, Computer Vision, Mathematical Modeling, Mechanics, Python Programming, Algorithms, Electronic Components

      Mixed · Course · 1 - 3 Months

    • Duke University

      Databricks to Local LLMs

      Skills you'll gain: Databricks, Generative AI, Data Lakes, Extract, Transform, Load, MLOps (Machine Learning Operations), Data Transformation, Data Pipelines, Large Language Modeling, Apache Spark, Analytics, Data Analysis, Data Ethics, Data Science, CI/CD, Data Governance

      4.4
      Rating, 4.4 out of 5 stars
      ·
      7 reviews

      Beginner · Course · 1 - 4 Weeks

    • Google Cloud

      Preparing for the Google Cloud Professional Data Engineer Exam 日本語版

      Skills you'll gain: MLOps (Machine Learning Operations), Data Processing, Data Pipelines, Data Infrastructure, Data Analysis, Google Cloud Platform, Data Architecture, Data Visualization, Data Modeling, Machine Learning, Data Warehousing, Cloud Security, Scalability, SQL

      4.7
      Rating, 4.7 out of 5 stars
      ·
      14 reviews

      Advanced · Course · 1 - 3 Months

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

    • Finalize a Data Science Project: CertNexus
    • Introduction to Vertex AI : Fractal Analytics
    • Smart Analytics, Machine Learning, and AI on GCP en Español: Google Cloud
    • Follow a Machine Learning Workflow: CertNexus
    • Beginning Llamafile for Local Large Language Models (LLMs): Duke University
    • Machine Learning with TensorFlow on Google Cloud en Français: Google Cloud
    • Large Language Models: H2O.ai
    • 컴퓨터 비전 분야에서의 딥 러닝 응용 사례: University of Colorado Boulder
    • Foundations of Local Large Language models: Duke University
    • 로봇공학: 캡스톤 디자인: University of Pennsylvania

    Frequently Asked Questions about Mlops

    MLOps, also known as DevOps for machine learning, is a practice that combines machine learning (ML) and software engineering to help organizations successfully manage and deploy ML models into production. It focuses on integrating the development, testing, and deployment of ML models with the overall software development lifecycle.

    MLOps aims to address the challenges associated with the production deployment of ML models, including version control, reproducibility, scalability, monitoring, and ongoing maintenance. It involves using various tools and techniques to streamline the ML model development process and ensure its smooth deployment and operation in real-world applications.

    By leveraging MLOps practices, organizations can accelerate the development and deployment of ML models, reduce the time and effort required for maintenance, and improve the overall reliability and performance of ML systems. It enables data scientists and ML engineers to collaborate effectively with software developers and operations teams, resulting in the efficient delivery of scalable and robust ML solutions.

    In summary, MLOps plays a crucial role in enabling organizations to effectively operationalize and scale their machine learning initiatives, ensuring that ML models are deployed and maintained in a sustainable and reliable manner.‎

    To pursue a career in MLOps (Machine Learning Operations), there are several skills you should consider learning:

    1. Machine Learning (ML) Fundamentals: Understanding the underlying concepts and techniques of machine learning is crucial for MLOps. This includes knowledge of algorithms, regression, classification, clustering, and more.

    2. Programming Languages: Proficiency in programming languages like Python and R is essential. These languages are widely used in machine learning and data science, enabling you to build ML models and automate processes.

    3. Data Engineering: MLOps involves managing and processing large volumes of data. Learning about data engineering, data pipelines, and working with databases (e.g., SQL) will help you efficiently handle data in an ML context.

    4. Cloud Computing: Familiarizing yourself with cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure will be beneficial. MLOps commonly leverages cloud resources for scalability and flexibility.

    5. Containerization and Orchestration: Understanding containerization technologies like Docker and orchestration tools like Kubernetes is crucial for deploying and managing ML models in production environments.

    6. DevOps Practices: Adopting DevOps practices like version control (e.g., Git), continuous integration/continuous deployment (CI/CD), and infrastructure automation will help you streamline ML workflows and collaboration.

    7. Knowledge of ML Frameworks: Familiarity with popular machine learning frameworks like TensorFlow, PyTorch, or scikit-learn is important. These frameworks facilitate building, training, and deploying ML models.

    8. Monitoring and Managing Models: Gaining knowledge of model performance monitoring, logging, and managing ML models in real-world scenarios helps ensure their efficiency, reliability, and accuracy.

    9. Communication and Collaboration: MLOps often involves working with cross-functional teams. Enhancing your communication and collaboration skills will aid in effectively conveying insights, requirements, and collaborating on ML projects.

    10. Continuous Learning: The field of MLOps is ever-evolving. Staying updated with new tools, techniques, and advancements in machine learning and data infrastructure is essential for continuous growth.

    Remember, MLOps is an interdisciplinary field that combines machine learning, software engineering, and operations. By acquiring these skills, you'll be well-equipped to thrive in the MLOps domain.‎

    With MLOps (Machine Learning Operations) skills, you can pursue a variety of job roles in the technology industry. Some of the job positions you can target include:

    1. Machine Learning Engineer: As a Machine Learning Engineer with MLOps skills, you will work on building, deploying, and maintaining machine learning models in production environments. Your expertise in MLOps will be crucial in managing the end-to-end lifecycle of machine learning applications.

    2. Data Scientist: Data scientists with MLOps skills have an edge as they can effectively scale and operationalize machine learning models. You will be responsible for analyzing complex datasets, developing and deploying ML models, and collaborating with cross-functional teams.

    3. MLOps Engineer: This role specifically focuses on deploying and maintaining machine learning models at scale. As an MLOps Engineer, you will design infrastructure, automate workflows, and ensure efficient deployment, monitoring, and maintenance of ML systems.

    4. AI Solution Architect: AI Solution Architects with MLOps skills are responsible for designing and implementing scalable AI solutions. They collaborate with data scientists and engineers to ensure the successful deployment and management of AI models in a production environment.

    5. Data Engineer: MLOps skills can be invaluable for data engineers working on big data projects. With these skills, you can streamline the process of preparing, processing, and managing large datasets for machine learning applications.

    6. DevOps Engineer: MLOps skills align well with the responsibilities of DevOps engineers. You will be involved in building and maintaining infrastructure, automating deployments, ensuring scalability, and implementing monitoring solutions for machine learning models.

    7. Cloud Architect: As a Cloud Architect with MLOps skills, you can help organizations design and implement cloud-based ML infrastructure. You will work on provisioning cloud resources, optimizing ML workloads, and ensuring security and scalability.

    These are just a few examples of the job roles that can be pursued with MLOps skills. The demand for professionals with these skills is constantly growing as more organizations adopt machine learning technologies, making it an exciting and promising field to explore.‎

    People with a strong foundation in mathematics, statistics, and computer science are best suited for studying MLOps. Additionally, individuals with an interest in machine learning, data analysis, and software development would find MLOps to be a good fit. This field requires a blend of technical skills and a deep understanding of data management, model training, deployment, and monitoring.‎

    Here are some topics related to MLOps that you can study:

    1. Machine Learning: Understanding the underlying concepts and techniques of machine learning is essential for MLOps. This includes topics like regression, classification, clustering, and natural language processing.

    2. Software Engineering: Developing a strong foundation in software engineering principles and practices will help you build robust and scalable solutions for deploying and managing machine learning models in production.

    3. DevOps: Learning about DevOps practices, tools, and methodologies will enable you to integrate machine learning models seamlessly into the software development lifecycle. Focus on topics such as continuous integration and continuous deployment (CI/CD), containerization, and infrastructure automation.

    4. Cloud Computing: Familiarize yourself with cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). Understanding cloud infrastructure, services, and deployment options will be crucial for implementing MLOps solutions.

    5. Data Engineering: Gain knowledge in data engineering concepts, such as data pipelines, data warehouses, and data processing frameworks like Apache Spark. This will help you prepare and transform data for machine learning models.

    6. Model Deployment and Monitoring: Explore topics like container orchestration with Kubernetes, managing model versions, and designing A/B testing frameworks to ensure the smooth deployment and monitoring of machine learning models.

    7. Data Governance and Ethics: Understanding the ethical and legal aspects of handling data, privacy regulations, bias mitigation, and fair use of machine learning models is essential for a responsible and successful MLOps practice.

    8. Performance Optimization: Learn techniques to optimize the performance and scalability of machine learning models. Topics like model pruning, quantization, and distributed training will help you deploy efficient and effective models.

    Remember, MLOps is an evolving field, so staying up-to-date with the latest tools, technologies, and research papers is equally important.‎

    Online MLOps courses offer a convenient and flexible way to enhance your knowledge or learn new MLOps, also known as DevOps for machine learning, is a practice that combines machine learning (ML) and software engineering to help organizations successfully manage and deploy ML models into production. It focuses on integrating the development, testing, and deployment of ML models with the overall software development lifecycle.

    MLOps aims to address the challenges associated with the production deployment of ML models, including version control, reproducibility, scalability, monitoring, and ongoing maintenance. It involves using various tools and techniques to streamline the ML model development process and ensure its smooth deployment and operation in real-world applications.

    By leveraging MLOps practices, organizations can accelerate the development and deployment of ML models, reduce the time and effort required for maintenance, and improve the overall reliability and performance of ML systems. It enables data scientists and ML engineers to collaborate effectively with software developers and operations teams, resulting in the efficient delivery of scalable and robust ML solutions.

    In summary, MLOps plays a crucial role in enabling organizations to effectively operationalize and scale their machine learning initiatives, ensuring that ML models are deployed and maintained in a sustainable and reliable manner. skills. Choose from a wide range of MLOps courses offered by top universities and industry leaders tailored to various skill levels.‎

    Choosing the best MLOps course depends on your employees' needs and skill levels. Leverage our Skills Dashboard to understand skill gaps and determine the most suitable course for upskilling your workforce effectively. Learn more about Coursera for Business here.‎

    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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