Build, Train and Deploy Machine Learning Models on AWS with Amazon SageMaker - AWS Online Tech Talks
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- Опубліковано 20 сер 2024
- To learn more, please visit: aws.amazon.com...
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. In this tech talk, we will introduce you to the concepts of Amazon SageMaker including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment of ML models. With zero setup required, Amazon SageMaker significantly decreases your training time and the overall cost of getting ML models from concept to production.
Learning Objectives:
- Learn the fundamentals of building, training & deploying machine learning models
- Learn how Amazon SageMaker provides managed distributed training for machine learning models with a modular architecture
- Learn to quickly and easily build, train & deploy machine learning models using Amazon SageMaker Subscribe to AWS Online Tech Talks On AWS:
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Just a word of warning, this is one of 2 ways to use SageMaker. There is now a SageMaker studio, which simplifies things. However, the concepts in the video is still helpful. I am just learning SageMaker now.
Can you please share some resources that you found as useful and easy to follow for Sagemaker Studio.
Just want to say this is high quality, succinct and very useful. Thank you so much! David
Excellent video. I have watched tons of other videos on Sagemaker, none are as descriptive as this one along with a real life scenario. Really loved it and learnt loads of stuff.
Still very useful even 5 years later! Obviously SageMaker console and features have changed a lot. But underlying concepts are explained very well. One of the 5% of AWS videos that are actually useful.
😊 ☁️ 🤝
One of the best video I have ever seen on a Amazon Sagemaker.
Explained clearly. Thanks a lot!
33:45 _Keeping Up With The Kardashians_ correlates best with a random sting of nonsense. *SURPRISE*
When did they go over deploying to production?
28:50 *Gory Horror* I see _Ru Pauls Drag Race_ is there, as it should be.
Very good video, very informative. Thanks for sharing it..
Very easy to understand!
Very useful content, Thanks for the video !!
I came to learn about deploying ML models and all I got was a 101 data science tutorial on how to find scary sherlock holmes programmes
Good video
Please help me understand this specific example. You've trained your model based on the title of the movie that the user reviewed? But it doesn't take into account the rating the user gave the movie? Is this right? So if a user has 10 movies in their rating history and they are all 1 star, but the names are similar, your model will recommend other movies that this user will probably not like? Am I missing something with this particular model? Wouldn't it be better to correlate the rating (# of stars) with the title similarities? Thanks!!
This video is recommendation for cold start problem. Cold start as in user does not have any history of rating. Ideally this happens for all the new users/first time users. Hence there is no ratings given by this user, It can not consider.
great talk
Good video.
Is the notebook code publicly available? that would be awesome
I trained with transfer learning from tf hub, deployed it on an ec2 instance, integrated it with my application, but can't seem to figure out how to get it deployed with sagemaker instead of manually putting it on our server - could be easier to bring your own model.
is ur problem solved, just a beginner who was curious in your case
Is this is the free content of aws online courses or just a refrence lectures
Awesome
This content is exceptional; it reminded me of a book I read with equally intriguing themes. "AWS Unleashed: Mastering Amazon Web Services for Software Engineers" by Author Name
Not able to use one of the OpenCV command cv2.imshow to view the video in was Aws juypter notebook. It shows error kernel dead please restart the kernal. Please help.
Hey i am having an error after deploying my model. I am trying to test my model but then I get "CancelledError: Session has been closed."
I have an output manifest file after labeling job of my image set, however while training I'm getting error for the output.manifest file, anyone faced similar issue?
During LEX REBUILD, for my AWS Chatbot, I have the error, it said: “Rebuilding: FAILED: An error occurred (ValidationException) When calling the UdatedSlotType operation; 1 validation err detected: Value at ‘slotTypeValues’ failed to satisfy constraint: Member must have a length less or equal to 10000”. There was no warning like other errors. How can I locate the error among many QnA pair to fix this one? --- Please help. TechSupport from AWS is not helpful so far after several requests.
I apologize for your frustration, Paideia. I was unable to locate any further information, but I did forward the info you provided to our QnABot team for review. I also suggest checking with our community of experts on re:Post for additional guidance: go.aws/aws-repost. ^BD
hi guys , i am badly looking for some course with hands on experience any suggestion is appriciated.
how to make a model/upload notebook from scratch ?
How do you install tesseract in 5.0.0.20201127-alpha in sage maker !!!!
How do you load from Redshift
Where can i get the slides?
A
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G DEMO.
Thanks.
when will i get rid of coding!!!
What’s what that awful lip smacking noise every 13 seconds?
This video could be reduced to 20 minutes with the amount of times there was an “uh”. Maybe an AI model to detect those and cut them out 🤔😂 Good work but seriously I couldn’t finish it.