From Data to Deployment
Become a pro in MLOps and develop fast and robust Machine Learning Services.
✅ Learn to build standardized ML Services step by step
✅ Insights about methods and tools used by other tech companies
✅ Apply framework to your ML project
Standardise Machine Learning Lifecycles
Many small and large companies struggle to standardise ML lifecycles across their organisation. This can have many reasons. One is due to the prioritisation of growing the team instead of optimising processes. Another reason is that too many contributors work in silos so that all develop their individual best practices. This may lead to inconsistent delivery quality, difficulties in maintaining pipelines and models as well as longer time to develop Machine Learning services.
Therefore, it is important that Data Science teams define all relevant steps in a Machine Learning lifecycle and to setup processes around each individual step. The use of frameworks, reusable code or checklists enable organisations to apply all expected steps in a more structured and easy way that saves a lot of time in the longrun.
- Introduction to ML lifecycles and MLOps
- What can be standardised, what not?
- Learn about a framework that helps to standardise the process of designing, developing and maintaining ML services
- Get step-by-step guidance and hear from real examples
- Practical exercises and discussions to apply the framework to your own Machine Learning project
- Get a bundle of materials to reuse in your projects
✅ 4 hours online live workshop together with our workshop host
✅ Learn about a framework for MLOps and apply a step-by-step guide to develop ML models
✅ Learn from the first-hand experiences of the workshop host
✅ Learn, discuss and get support by a group of peers
The workshop is designed for data professionals from mid-level to lead position who often run into challenges when multiple people work on the same ML project.
Data Scientist / ML Engineer / Data Analyst
who wants to learn how to build robust ML services.
Data Product Manager / Team Leads / Tech Leads
who wants to standardise ML processes in order to deliver data products faster and of higher quality.
Data Science Teams
that are working on highly sensitive use cases (e.g. fraud, credit) where ML services need to work 24/7 without any risk of failure.
Tino works as Senior Data Scientist at shopify where he takes care of A/B experimentation, data modeling and MLOps. Besides, he is a self-deployed data consultant and about to publish his first e-Book about standardised ML lifecycles. Tino started his professional career as a Project Manager in a Berlin based startup before he pivoted into Data Science. However, his background and experience in business has always helped him to turn his data skills into impactful business value. Before moving to Shopify, Tino worked as a Data Scientist at N26.
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