Machine Learning using AWS SageMaker-training-in-bangalore-by-zekelabs

Machine Learning using AWS SageMaker Training

Machine Learning using AWS SageMaker Course: Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don't have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, Amazon SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a single click from the Amazon SageMaker console. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments. A complete hands-on course for anybody with fundamental understanding of machine learning.
Machine Learning using AWS SageMaker-training-in-bangalore-by-zekelabs
Assignments
Machine Learning using AWS SageMaker-training-in-bangalore-by-zekelabs
Industry Level Projects
Machine Learning using AWS SageMaker-training-in-bangalore-by-zekelabs
Certification

Machine Learning using AWS SageMaker Course Curriculum



Understanding Machine Learning Pipeline
SageMaker for Machine Learning
AWS account creation
AWS S3 bucket creation
Data collection, transformation & upload to S3
Model Deployment
Common Information
K-Means Algorithms
Neural Topic Model
Random Cut Forest
XGBoost Algorithm
Custom Interface Code
Use Algorithm & Model Package Resource
Monitor & Analyze Training jobs
Hyperparameter Tuning
Interface Pipeline
Elastic Interface
Standard Practices
Apache Spark with Amazon SageMaker
Apache MXNet with Amazon SageMaker
PyTorch with Amazon SageMaker
Monitoring with CloudWatch
Logging in SageMaker API Calls with AWS CloudTrail
Data Labeling
Workforce for labeling - public,private,vendor
Understanding boto3
DataType

Frequently Asked Questions


We have options for classroom-based as well as instructor led live online training. The online training is live and the instructors screen will be visible and voice will be audible. Your screen will also be visible and you can ask queries during the live session.

The training on "Machine Learning using SageMaker" course is a hands-on training. All the code and exercises will be done in the live sessions. Our batch sizes are generally small so that personalized attention can be given to each and every learner.

We will provide course-specific study material as the course progresses. You will have lifetime access to all the code and basic settings needed for this "Machine Learning using SageMaker" through our GitHub account and the study material that we share with you. You can use that for quick reference

Feel free to drop a mail to us at [email protected] and we will get back to you at the earliest for your queries on "Machine Learning using SageMaker" course.

We have tie-ups with a number of hiring partners and and placement assistance companies to whom we connect our learners. Each "Machine Learning using SageMaker" course ends with career consulting and guidance on interview preparation.

Minimum 2-3 projects of industry standards on "Machine Learning using SageMaker" will be provided.

Yes, we provide course completion certificate to all students. Each "Machine Learning using SageMaker" training ends with training and project completion certificate.

You can pay by card (debit/credit), cash, cheque and net-banking. You can also pay in easy installments. You can reach out to us for more information.

We take pride in providing post-training career consulting for "Machine Learning using SageMaker".



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