Edchart Certified

AWS Machine Learning SME

23

The AWS Cloud Machine Learning Certification is designed for individuals who want to demonstrate their expertise in designing, building, and deploying machine learning solutions using AWS cloud services. This certification validates their knowledge and skills in various machine learning techniques, AWS services like Amazon SageMaker, and the ability to optimize and deploy machine learning models on the AWS platform.

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# Take your Exam at free of cost.
# To cliam the certificate Minimal charges will be applicable.
# Charges inclusive of Credly Digital Credential as well.


India:
Rs.5000.00 Rs.950.00
Other
Countries:
$75.00 $18.00

Importance of AWS Cloud Machine Learning:

  1. Scalability: AWS Cloud Machine Learning provides the scalability needed to train and deploy machine learning models on large datasets. It allows organizations to leverage the power of cloud computing to process and analyze massive amounts of data efficiently.

  2. Cost-Effectiveness: With AWS Cloud Machine Learning, organizations can take advantage of the pay-as-you-go model, where they only pay for the resources used during the training and deployment of machine learning models. This helps control costs and makes machine learning more accessible to businesses of all sizes.

  3. Ease of Use: AWS offers a range of managed machine learning services, such as Amazon SageMaker, that simplify the process of building, training, and deploying machine learning models. These services provide pre-built algorithms, data preparation tools, and infrastructure management, reducing the complexity and time required to develop machine learning solutions.

  4. Integration with AWS Services: AWS Cloud Machine Learning seamlessly integrates with other AWS services, allowing organizations to leverage their existing data infrastructure and services. This integration enables easy data ingestion, storage, and real-time predictions using machine learning models.

  5. Flexibility and Customization: AWS Cloud Machine Learning offers a wide range of machine learning frameworks, libraries, and tools, giving organizations the flexibility to choose the right technology for their specific use cases. This allows for custom model development and implementation tailored to the organization's needs.

Benefits of AWS Cloud Machine Learning Certification:

  1. Industry Recognition: Certification validates professionals' expertise in designing and implementing machine learning solutions on the AWS platform, enhancing their credibility and recognition in the field of machine learning.

  2. Career Advancement: Holding an AWS Cloud Machine Learning Certification opens up new career opportunities in machine learning engineering, data science, and AI-related roles. It demonstrates proficiency in building scalable and efficient machine learning solutions on the AWS cloud.

  3. Enhanced Skills and Knowledge: The certification process involves hands-on experience with AWS machine learning services and frameworks, expanding professionals' skills and knowledge in the field of machine learning.

  4. Competitive Edge: Certification provides a competitive edge in the job market by showcasing proficiency in AWS machine learning services, which are widely adopted by organizations worldwide.

  5. Networking and Collaboration: Certified professionals gain access to a community of experts and peers, fostering networking opportunities, collaboration, and knowledge sharing in the field of machine learning and AI.

Skills Required for AWS Cloud Machine Learning Certification:

  1. Machine Learning Concepts: Understanding of core machine learning concepts, algorithms, and techniques, such as supervised learning, unsupervised learning, reinforcement learning, and neural networks.

  2. AWS Machine Learning Services: Proficiency in AWS machine learning services, including Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, and Amazon Forecast, and their capabilities for model development, training, and deployment.

  3. Data Manipulation and Preparation: Skills in data manipulation and preprocessing techniques, such as data cleaning, feature engineering, and handling missing values, to prepare datasets for training machine learning models.

  4. Model Evaluation and Optimization: Knowledge of model evaluation metrics, hyperparameter tuning, and optimization techniques to improve the performance and accuracy of machine learning models.

  5. Programming and Scripting: Proficiency in programming languages such as Python and frameworks like TensorFlow or PyTorch for developing machine learning models and implementing data preprocessing pipelines.

Exam Timing: 60 (Mins)
Pass Per 60%
Level Expert
Roles AWS Machine Learning
No. of Questions 30
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