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విజ్ఞాన శాస్త్ర సాంకేతిక పరిశోధనా సంస్థ / विज्ञान शास्त्र प्रौद्योगिकी और परिशोधन संगठन
Events
Workshop
AWS Cloud for Machine Learning
Oct
11
Friday
10:00
AM
- 12:00
PM
Summary
AWS Cloud for Machine Learning refers to a suite of cloud-based services and tools provided by Amazon Web Services that enable developers and data scientists to build, train, and deploy machine learning models at scale. It encompasses services like Amazon SageMaker for model development, Amazon S3 for data storage, and various AI services for specific tasks. This platform allows users to leverage the flexibility, scalability, and computational power of the cloud to accelerate machine learning workflows, streamline data management, and enhance collaboration across teams.
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About Workshop
Here are some key points that were covered in the two-day workshop on AWS Cloud for Machine Learning.

1. Introduction to AWS Cloud:
• Overview of AWS services and infrastructure.
• Benefits of using cloud computing for machine learning.
2. AWS Machine Learning Services:
• Introduction to Amazon SageMaker.
• Overview of AWS Lambda, Amazon S3, and other relevant services.
3. Data Preparation and Management:
• Techniques for data collection, storage, and processing in the cloud.
• Using Amazon S3 for data storage and management.
4. Model Development and Training:
• Building and training machine learning models with SageMaker.
• Understanding Jupyter notebooks and their integration with SageMaker.
5. Model Deployment:
• Steps to deploy machine learning models on AWS.
• Using SageMaker for real-time and batch predictions.
6. Monitoring and Optimization:
• Techniques for monitoring model performance.
• Best practices for optimizing model accuracy and efficiency.
7. Case Studies and Real-World Applications:
• Discussion of successful machine learning projects using AWS.
• Industry-specific applications and use cases.
8. Hands-On Labs:
• Practical exercises to build and deploy a simple machine learning model.
• Step-by-step guidance for participants to implement learned concepts.
9. Security and Compliance:
• Best practices for ensuring data security and compliance in cloud-based ML projects.
10. Future Trends in Cloud ML:
• Emerging trends and technologies in machine learning and cloud computing.
• Discussion on the future of AI and machine learning on AWS.
11. Q&A Session:
• Open forum for participants to ask questions and discuss challenges.
12. Key Components:
• Data Collection: Methods for gathering data from various sources, including databases, APIs, and web scraping.
• Data Cleaning: Techniques to pre-process and clean data to ensure accuracy and reliability.
Exploratory Data Analysis (EDA): Tools and techniques for visualizing and summarizing data to identify patterns and anomalies.


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Venue
Cyber Security Lab-606
Dept.of Advanced Computer Science and Engineering
About Speakers
Mr. Akram
Cloud Engineer
Event Coordinator
Dr. Jyostna Devi Bodapati
ASSOCIATE PROFESSOR
Contact Us
Vignan's Foundation for Science, Technology and Research
(Deemed to be University), Vadlamudi, Guntur-522213
info@vignan.ac.in
0863-2344700 / 701
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