Industry Impact: Professional Certificate in DevOps Machine Learning in Action
-- viewing nowDevOps professionals looking to enhance their skills in machine learning can benefit from this certificate program. This DevOps Machine Learning in Action certificate program is designed for professionals who want to bridge the gap between software development and operations.
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About this course
Through hands-on training and real-world projects, learners will gain expertise in implementing machine learning models in a DevOps environment.
Key topics covered include data preprocessing, model selection, and deployment using popular tools like Docker and Kubernetes.
By the end of this program, learners will be able to design and implement efficient machine learning pipelines in a DevOps setting.
Take the first step towards upskilling your DevOps career and explore this program further to learn more about the curriculum and admission requirements.
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Course details
• Introduction to DevOps and Machine Learning: This unit covers the fundamentals of DevOps and machine learning, including their applications, benefits, and challenges. It provides an overview of the course and sets the stage for the rest of the program.
• Agile Methodologies and Version Control: This unit focuses on agile methodologies and version control systems, including Git and GitHub. It teaches students how to use these tools to manage code and collaborate with team members.
• Containerization and Orchestration: This unit covers containerization using Docker and orchestration using Kubernetes. It teaches students how to create, deploy, and manage containers and containerized applications.
• Machine Learning Fundamentals: This unit introduces students to the basics of machine learning, including supervised and unsupervised learning, regression, classification, and clustering. It covers the mathematical and statistical concepts underlying machine learning.
• Data Preprocessing and Feature Engineering: This unit focuses on data preprocessing and feature engineering, including data cleaning, feature scaling, and dimensionality reduction. It teaches students how to prepare data for machine learning models.
• Model Selection and Hyperparameter Tuning: This unit covers model selection and hyperparameter tuning, including the use of cross-validation and grid search. It teaches students how to select the best machine learning model for a given problem and tune its hyperparameters.
• Model Deployment and Monitoring: This unit focuses on model deployment and monitoring, including the use of containerization and orchestration tools. It teaches students how to deploy machine learning models in production and monitor their performance.
• DevOps Tools and Practices: This unit covers DevOps tools and practices, including continuous integration and continuous deployment (CI/CD). It teaches students how to use these tools to automate the build, test, and deployment of software.
• Machine Learning in Action: This unit applies machine learning concepts to real-world problems, including image classification, natural language processing, and recommender systems. It teaches students how to use machine learning to solve practical problems.
• Project Development and Presentation: This unit focuses on project development and presentation, including the use of agile methodologies and version control systems. It teaches students how to develop and present a machine learning project from start to finish.
Career path
**Industry Impact: Professional Certificate in DevOps Machine Learning in Action**
**Job Market Trends in the UK**
**Career Roles in DevOps Machine Learning**
* **DevOps Engineer**: A DevOps Engineer is responsible for ensuring the smooth operation of software systems, from development to deployment. They bridge the gap between development and operations teams, implementing automation and continuous integration/continuous deployment (CI/CD) pipelines. **Primary keywords:** DevOps, Automation, CI/CD.
* **Machine Learning Engineer**: A Machine Learning Engineer designs, develops, and deploys machine learning models to solve complex problems. They work with large datasets, implement algorithms, and integrate models into production environments. **Primary keywords:** Machine Learning, AI, Data Science.
* **Data Scientist**: A Data Scientist collects, analyzes, and interprets complex data to gain insights and inform business decisions. They develop predictive models, create data visualizations, and communicate findings to stakeholders. **Primary keywords:** Data Science, Analytics, Business Intelligence.
* **Cloud Engineer**: A Cloud Engineer designs, builds, and maintains cloud-based systems, ensuring scalability, security, and reliability. They work with cloud platforms like AWS, Azure, or Google Cloud. **Primary keywords:** Cloud Computing, Cloud Security, Scalability.
* **AI/ML Engineer**: An AI/ML Engineer develops and deploys artificial intelligence and machine learning models to solve real-world problems. They work with computer vision, natural language processing, and predictive analytics. **Primary keywords:** AI, Machine Learning, Deep Learning.
Entry requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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INDUSTRY IMPACT: PROFESSIONAL CERTIFICATE IN DEVOPS MACHINE LEARNING IN ACTION
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London School of Planning and Management
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05 May 2025
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