Course Outline
Introduction
- Understanding machine learning with SageMaker
- Machine learning algorithms
Overview of AWS SageMaker Features
- AWS and cloud computing
- Models development
Setting up AWS SageMaker
- Creating an AWS account
- IAM admin user and group
Familiarizing with SageMaker Studio
- UI overview
- Studio notebooks
Preparing Data Using Jupyter Notebooks
- Notebooks and libraries
- Creating a notebook instance
Training a Model with SageMaker
- Training jobs and algorithms
- Data and model parallel trainings
- Post-training bias analysis
Deploying a Model in SageMaker
- Model registry and model monitor
- Compiling and deploying models with Neo
- Evaluating model performance
Cleaning Up Resources
- Deleting endpoints
- Deleting notebook instances
Troubleshooting
Summary and Conclusion
Requirements
- Experience with application development
- Familiarity with Amazon Web Services (AWS) Console
Audience
- Data scientists
- Developers
Testimonials
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease
The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama
The trainer very easily explained difficult and advanced topics.
Leszek K
All like it
蒙 李
Communication with lecturers
文欣 张
like it all
lisa xie
I genuinely liked excercises
- L M ERICSSON LIMITED
I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
The Jupyter notebook form, in which the training material is available
- L M ERICSSON LIMITED
There were many exercises and interesting topics.
- L M ERICSSON LIMITED
Some great lab exercises analyzed and explained by the trainer in depth (e.g. covariants in linear regression, matching the real function)
- L M ERICSSON LIMITED
It's just great that all material including the exercises is on the same page and then it gets updated on the fly. The solution is revealed at the end. Cool! Also, I do appreciate that Krzysztof took extra effort to understand our problems and suggested us possible techniques.
Attila Nagy - L M ERICSSON LIMITED
It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback
Kamila Begej - GE Medical Systems Polska Sp. Zoo
I like that training was focused on examples and coding. I thought that it is impossible to pack so much content into three days of training, but I was wrong. Training covered many topics and everything was done in a very detailed manner (especially tuning of model's parameters - I didn't expected that there will be a time for this and I was gratly surprised).
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Issues discussed, exercises carried out (examples), atmosphere of training, contact with the trainer, location.
- Wojskowe Zakłady Uzbrojenia S.A. w Grudziądzu
I like that it focuses more on the how-to of the different text summarization methods
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Ewa has a passion for the subject and a huge wealth of knowledge. She impressed all of us with her knowledge and kept us all focused through the day.
Rock Solid Knowledge Ltd
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
Richard Blewett - Rock Solid Knowledge Ltd
So much breadth and topics covered. I felt it was a huge subject to try and cover in 3 days - the trainer did what they could to cover everything almost exactly on time!
Rock Solid Knowledge Ltd
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
convolution filter
Francesco Ferrara - Inpeco SpA
Machine learning, python, data manipulation
Siphelo Mapolisa - University Of South Africa
The theoretical explanations
Molatelo Tloubatla - University Of South Africa
The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.
Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH
I like that it focuses more on the how-to of the different text summarization methods