MLOps
Streamline your AI and ML workflows with MLOps. Learn to deploy, manage, and scale models in production environments with this comprehensive course.
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Contact our teamWhat will you learn?
Dive into the world of MLOps and discover its importance in scaling AI and machine learning models for production. This course will guide you through deploying models, automating workflows, and maintaining their performance over time. Learn to monitor and scale systems, ensuring reliability in diverse production environments. Case studies will demonstrate real-world applications, giving you insights into industry practices.
Key takeaways
- Understand MLOps fundamentals
- Learn model deployment techniques
- Automate ML workflows
- Monitor and scale production models
Program
- Introduction to MLOps: Understand its role in modern AI and ML workflows
- Model Deployment: Learn strategies for deploying models in production.
- Automation: Explore tools to automate the ML lifecycle.
- Monitoring and Scaling: Ensure reliability in real-world applications.
- Case Studies: Real-life examples of MLOps success stories.
Who is it for?
This course is ideal for data engineers, AI practitioners, and tech enthusiasts eager to manage and optimize machine learning models in production.
Requirements
Basic knowledge of the topic is beneficial.
Why should I do this training?
Practical skills
Learn to automate and manage ML workflows
Scalability focus
Master techniques for reliable production systems
Industry alignment
Explore MLOps practices used globally
What does it look like?
What else
should I know?
After registering, you will receive a confirmation email with practical information.
See you soon!
Course information
Hands-on with tools like Docker and MLflow
Explore cloud options like AWS SageMaker
Real-world case studies included
Duration: 12-14 hours
