MLOps/DataOps Master Class

Training on MLOps and DataOps concepts with hands-on practicals

MLOps DataOps

What you will learn and the outcome

  • Insightful and conceptual live training on the concepts and tools in MLOps and DataOps for Machine Learning workflows
  • Hands on session for end to end machine learning with different MLOps and DataOps tools and frameworks
  • Access to the course material
  • Access to online platform for AI/ML operations
  • Certificate of achievement

Delivery mode 

  • You can choose to join either on-site training in Adelaide, or join online from anywhere virtually
  • 2 hours x 2 sessions 

Best suited for

  • Professionals (Engineering, Healthcare, Education, Industrial, Government, etc) who is looking forward to develop workflows for Machine Learning
  • Business executives (C-level, directors, management) who would like to have advanced understanding of MLOps and DataOps tools/frameworks
  • University students
  • School leavers
  • High school students
  • Any enthusiasts

Pre-requisite

  • Ability to understand scientific concepts
  • Computer literacy 
  • Basic understanding of Artificial Intelligence and Machine Learning

Your trainer

 Dr. Kalana Withanage has Ph.D in Computer Vision / Machine Learning BSc. (Hons) in Electrical and Information Engineering 16+ years industrial experience   in architecting and developing solutions with machine learning, computer vision, robotics, and embedded systems. He is an inspiring trainer and   international community educational event speaker.

Course content

 Session 1

 Machine learning overview

 Need for MLOps and DataOps

 Key tasks in MLOps and DataOps

 Data cleaning, data labelling, data versioning

 Model training, model fine-tuning

 Model deployment, monitoring, versioning

 DataOps and MLOps CI/CD/CT automation

 Intro to tools: LakeFS, MLFlow, DVC, Kubeflow, Flyte

 Hands-on session on: end to end model development and deployment


Session 2

 Deployment tools: Kubernetes, Docker

 On premises, Cloud, and Hybrid ML workflow deployment

 GPU resource setup

 Model performance monitoring 

 Automated retraining

 Auto-scaling infrastructure for training and deployment

 Hands-on session on model performance analysis, monitoring, and retraining