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Course Outline
Introduction
- Machine Learning models vs traditional software
Overview of the DevOps Workflow
Overview of the Machine Learning Workflow
ML as Code Plus Data
Components of an ML System
Case Study: A Sales Forecasting Application
Accessing Data
Validating Data
Data Transformation
From Data Pipeline to ML Pipeline
Building the Data Model
Training the Model
Validating the Model
Reproducing Model Training
Deploying a Model
Serving a Trained Model to Production
Testing an ML System
Continuous Delivery Orchestration
Monitoring the Model
Data Versioning
Adapting, Scaling and Maintaining an MLOps Platform
Troubleshooting
Summary and Conclusion
Requirements
- An understanding of the software development cycle
- Experience building or working with Machine Learning models
- Familiarity with Python programming
Audience
- ML engineers
- DevOps engineers
- Data engineers
- Infrastructure engineers
- Software developers
35 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €11400 online delivery, based on a group of 2 delegates, €3600 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
Contact us for an exact quote and to hear our latest promotions
Public Training
Please see our public courses
Testimonials (3)
There were many practical exercises supervised and assisted by the trainer
Aleksandra - Fundacja PTA
Course - Mastering Make: Advanced Workflow Automation and Optimization
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
