AWS SageMaker Complete Course| PyTorch & Tensorflow NLP-2023

🎓 AWS SageMaker Complete Course | PyTorch & TensorFlow NLP - 2023

🚀 Course Headline: Build DL/ML models in Sklearn, Tensorflow/Keras, and PyTorch - Learn to containerize your algorithms with Docker and seamlessly integrate them into AWS SageMaker for real-world applications.

📚 Course Description:

Welcome to the Data Analytics Platform Academy's (DAP) comprehensive guide to mastering AWS SageMaker. This course is meticulously designed for learners who aspire to harness the full potential of AWS SageMaker to build, deploy, and manage machine learning models.

• Bring Docker Containers to AWS SageMaker: Learn how to package your on-premises deep learning models into Docker containers and smoothly transition them to AWS SageMaker for a scalable, production-ready deployment.
• Integrate Your Own Algorithms: Get hands-on experience with bringing custom algorithms from your local machine to the cloud and running them within the AWS SageMaker environment.
• Utilize Pre-Built Optimized SageMaker Algorithms: Explore the benefits of using AWS's pre-built, optimized algorithms to accelerate your model development process.

🔍 Deep Dive into ML Workflow and Pipeline Creation:

• Craft robust machine learning pipelines that enable model retraining and scheduling for automation and continuous improvement.
• Gain insights into creating a workflow for training and deploying models on AWS SageMaker.

🎫 AWS Certified Machine Learning Specialty (MLS-C01): This course aligns with the AWS Certified Machine Learning Specialty exam (MLS-C01), ensuring you understand the concepts necessary to pass this certification and excel in your machine learning career.

🚀 What You'll Learn:

• Understanding AWS SageMaker: Discover what SageMaker is, why it's crucial for machine learning projects, and how it can simplify the entire machine learning lifecycle.
• SageMaker ML Lifecycle: Dive into the end-to-end process of building, training, evaluating, deploying, and monitoring machine learning models with SageMaker.
• SageMaker Architecture: Get a comprehensive understanding of SageMaker's architecture, including its compute elements and the role they play in scaling your models.
• Training Techniques in SageMaker:
  • Learn how to integrate your own Docker container from an on-premises environment into AWS SageMaker.
  • Bring your own algorithms from a local machine to AWS SageMaker for model training and deployment.
  • Explore the use of pre-built algorithms available in SageMaker for quicker model development.
• SageMaker Pipeline Development: Create pipelines that allow for the reuse of code for data preparation, feature creation, model training, and deployment, with the ability to schedule and retrain models as needed.
• Training Notebooks in SageMaker: Learn how to use Jupyter notebooks on SageMaker to perform all aspects of model development and experimentation.

📅 Course Duration & Structure: Over 5 hours of content, this course is designed for beginners to advanced learners. It provides a step-by-step approach to understanding and implementing machine learning models in AWS SageMaker effectively.

• Hands-on Projects: Apply your knowledge through practical projects that mimic real-world scenarios.
• Interactive Quizzes & Assignments: Reinforce your learning with quizzes and hands-on tasks.
• Expert Instructors: Learn from industry experts who bring years of experience in AWS SageMaker and machine learning.

🎯 Target Audience:

• Machine Learning Engineers
• Data Scientists
• AI Enthusiasts
• Anyone looking to build a career in data science and machine learning on AWS platforms

Embark on your journey to mastering AWS SageMaker with this comprehensive course that combines theoretical knowledge with practical application. 🌟

Enroll now and transform the way you approach machine learning with AWS SageMaker!