Главная
Study mode:
on
1
Fully-Managed Notebook Instances with Amazon SageMaker - a Deep Dive
2
Built-in Machine Learning Algorithms with Amazon SageMaker - a Deep Dive
3
Bring Your Own Custom ML Models with Amazon SageMaker
4
Train Your ML Models Accurately with Amazon SageMaker
5
Deploy Your ML Models to Production at Scale with Amazon SageMaker
6
Tune Your ML Models to the Highest Accuracy with Amazon SageMaker Automatic Model Tuning
7
Scale up Training of Your ML Models with Distributed Training on Amazon SageMaker
8
Use the Deep Learning Framework of Your Choice with Amazon SageMaker
9
Learn to Analyze the Co-Relation in Your Datasets Using Feature Engineering with Amazon SageMaker
10
Get Scheduled Predictions on Your ML Models with Amazon SageMaker Batch Transform
11
Build Highly Accurate Training Datasets at Reduced Costs with Amazon SageMaker Ground Truth
12
Organize, Track, and Evaluate ML Training Runs With Amazon SageMaker Experiments
13
Automatically Build, Train, and Tune ML Models With Amazon SageMaker Autopilot
14
Deploy Multiple ML Models on a Single Endpoint Using Multi-model Endpoints on Amazon SageMaker
15
Amazon SageMaker Studio - A Fully Integrated Development Environment For Machine Learning
16
Analyze, Detect, and Get Alerted on Problems With Training Runs Using Amazon SageMaker Debugger
Description:
Dive deep into Amazon SageMaker's capabilities with this comprehensive technical series. Explore fully-managed notebook instances, built-in machine learning algorithms, and custom ML model integration. Learn to train models accurately, deploy to production at scale, and tune for highest accuracy using automatic model tuning. Master distributed training, utilize various deep learning frameworks, and analyze dataset correlations through feature engineering. Discover how to obtain scheduled predictions, build accurate training datasets cost-effectively, and organize ML training runs. Explore automated model building and tuning with Autopilot, deploy multiple models on single endpoints, and leverage the integrated development environment of SageMaker Studio. Gain insights into analyzing and debugging training runs for optimal performance.

Amazon SageMaker Technical Deep Dive Series

Amazon Web Services
Add to list
#Programming #Cloud Computing #Amazon Web Services (AWS) #Amazon SageMaker #Computer Science #Machine Learning #Feature Engineering #Model Deployment #Model Training #Distributed Training
0:00 / 0:00
1 of 16

Fully-Managed Notebook Instances with Amazon SageMaker - a Deep Dive