Amazon Web Service
Internship 2024
Career-Based Complete Amazon Web Service Internship Program. Get Insights into the fundamentals of AWS cloud services, and how to deploy machine learning models efficiently using AWS services like SageMaker, CI/CD pipelines, and AWS AI services concepts with Project Implementation and Assignments. Stay Updated on the Latest Industrial Updates.
1/ 2 Months
Online
8+ Live Projects
Dual Certification
Ultimate Step towards your Career Goals: Expert in Amazon Web Service
Get ahead with the FutureTech Industrial Internship Program: gain hands-on experience, connect with industry leaders, and develop cutting-edge skills. Earn a stipend, receive expert mentorship, and obtain a certificate to boost your career prospects. Transform your future with practical, real-world learning today
Internship Benifits
Mentorship
Receive guidance and insights from industry experts.
Hands-on Experience
Gain practical skills in a real-world cutting-edge projects.
Networking
Connect with professionals and peers in your field.
Skill Development
Enhance your technical and soft skills.
Career Advancement
Boost your resume with valuable experience.
Certificate
Get a certification to showcase your achievements.
Amazon Web Service - Internship Overview
AWS Introduction & Account Creation
- Overview of AWS and its global infrastructure.
- Setting up an AWS account and understanding IAM (Identity and Access Management).
- Understanding AWS Free Tier and managing costs.
AWS Cloud Essentials 1
- Virtual Machines (EC2): Introduction to EC2 instances, types, pricing, and configurations.
- Elastic Load Balancing (ELB): Basics of load balancing, types of load balancers.
- Elastic Block Storage (EBS): Understanding EBS volumes, snapshots, and attaching them to EC2 instances.
AWS Cloud Essentials 2
- AWS Lambda: Introduction to serverless computing and Lambda functions.
- Docker Containers: Basics of Docker, and how to run containers in AWS using ECS & EKS.
- Amazon ECS & EKS: Differences between ECS (Elastic Container Service) and EKS (Elastic Kubernetes Service) for container orchestration.
Introduction to Machine Learning
- Overview of machine learning concepts: supervised, unsupervised, and reinforcement learning.
- Setting up a machine learning workflow.
ML with AWS SageMaker
- Overview of AWS SageMaker for building, training, and deploying ML models.
- Understanding SageMaker Studio, notebooks, and built-in algorithms.
Data Collection & Pandas for Machine Learning
- Collecting and preparing data for ML.
- Using Pandas for data manipulation, cleaning, and exploratory analysis.
Data Engineering with AWS
- AWS Glue & Glue ETL: Introduction to Glue for data integration and ETL (Extract, Transform, Load) jobs.
- AWS Athena: Querying data directly from S3 using SQL with Athena.
ML Modeling & Evaluation
- Understanding model types (regression, classification).
Evaluation metrics: Accuracy, precision, recall, F1-score, confusion matrix.
AWS SageMaker Built-In Algorithms for ML
- Utilizing pre-built algorithms in SageMaker for various ML tasks like classification, regression, clustering, etc.
AWS SageMaker Canvas & Autopilot
- SageMaker Canvas: A no-code ML solution for business analysts to build ML models.
- SageMaker Autopilot: Automating ML model building with minimal user input.
Recurrent Neural Networks (RNN) & NLP Basics
- Introduction to RNNs and how they can be used for time-series data or sequential data like text.
- Basics of Natural Language Processing (NLP) and common use cases.
Version Control with Git & GitHub
- Basics of Git for version control.
- Setting up a GitHub repository for code management.
CI/CD with GitHub
- Introduction to Continuous Integration (CI) and Continuous Deployment (CD).
- Setting up a CI/CD pipeline with GitHub Actions to automate testing and deployment.
Deploying ML Models with AWS SageMaker
- Deploying trained models as endpoints in SageMaker for real-time inference.
- Understanding batch processing and real-time inference use cases.
CI/CD with SageMaker Pipeline
- Automating the ML workflow using SageMaker Pipelines.
- Managing data, model training, and deployment workflows using SageMaker’s pipeline features.
AWS AI Services Overview
- Introduction to Amazon’s suite of AI-powered services.
Amazon Rekognition
- Image and video analysis for facial recognition, object detection, and text in images.
Amazon Comprehend
- Natural Language Processing (NLP) for sentiment analysis, entity recognition, and language detection.
Amazon Polly
- Text-to-speech service to generate speech from text in multiple languages.
Amazon Lex
- Building conversational interfaces and chatbots using Lex.
Amazon Transcribe
- Speech-to-text service for transcribing audio files into text.
Looking for in-depth Syllabus Information? Explore your endless possibilities in Amazon Web Service with our Brochure!
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Project Submission: Example Output Screenshots from Our Clients
Take a look at these sample outputs crafted by our clients. These screenshots showcase the impressive results achieved through our courses and projects. Be inspired by their work and visualize what you can create!
Dual Certification: Internship Completion & Participation
Earn prestigious Dual Certification upon successful completion of our internship program. This recognition validates both your participation and the skills you have honed during the internship, providing a competitive edge in your professional journey. Show off your expertise with pride!
How does this Internship Program Work?
Step 1 Enroll in the Program
✅ Get a Mentor Assigned
✅Presentations & Practice Codes
✅ Learn at your Flexible Time
✅ Apprehend the concepts
Step 2 Project Development
✅ Implement Skills Learn
✅ Develop Projects with assistance
✅ Get Codes for Reference
✅ Visualise the Concepts
Step 3 Certification
✅ Get Certified
✅ Certificate of Internship
✅ Project Completion Certificate
✅ Share on social media
✅ Get Job Notifications
Internship Letter (LoR)
Internship Report
Flexible Schedules
1 Month
₹1999/-
1 Month
- Internship Acceptance Letter
- 90 Days from the date of payment
- 4 LIVE intractive Mastermind Sessions
- 4+ Capstone Projects & Codes
- Full Roadmap
- Internship Report
- 1 Month Internship Certificate
₹3299/-
2 Month
- Internship Acceptance Letter
- 180 Days from the date of payment
- 4 LIVE interactive Mastermind Sessions
- 12+ Capstone Projects & Codes
- Full Roadmap
- Internship Report
- Participation Certificate
- 2 Month Internship Certificate
Our Alumni Employers
Curious where our graduates make their mark? Our students go on to excel in leading tech companies, innovative startups, and prestigious research institutions. Their advanced skills and hands-on experience make them highly sought-after professionals in the industry.
FAQ
Will I get hands-on experience with AWS?
Yes. The course includes practical, hands-on labs where you’ll set up services like EC2, Lambda, SageMaker, and deploy ML models.
What is AWS SageMaker, and why is it important for machine learning?
AWS SageMaker is a fully managed service that provides tools to build, train, and deploy machine learning models. It simplifies and accelerates ML workflows, which makes it a key tool for professionals in this domain.
What are the costs associated with using AWS for machine learning?
AWS charges based on usage (compute, storage, data transfer). The course explains how to optimize costs using free-tier services, cost calculators, and efficient resource management.
How will this course help with ML model deployment?
The course covers how to deploy models on AWS using SageMaker and integrate CI/CD pipelines for continuous delivery. This allows for automated deployment and monitoring of ML models in production.
What is CI/CD, and why is it important in machine learning?
CI/CD stands for Continuous Integration and Continuous Deployment. It’s a practice in software development where code changes are automatically tested and deployed. In ML, this ensures models are continuously updated, tested, and deployed without manual intervention.
Can I apply these concepts in real-world projects?
Yes, you will work on practical projects like a census income prediction and a music recommender system, both of which you can showcase in your portfolio.
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