ML Ops using AWS
Internship 2024
Career-Based Complete ML Ops using AWS Internship Program. Get Insights into: Learn and Practice Pandas, Data Preprocessing, Model Creation, Model Evaluation, Deep Learning & Sci Kit Learn with Project Implementation and Assignments. Stay Updated on Latest Industrial Updates.
1/ 2 Months
Online
8+ Live Projects
Dual Certification
Ultimate Step towards your Career Goals: Expert in ML Ops using AWS Technologies
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.
ML Ops using AWS Internship Overview
AWS Overview & Account Creation: Understand AWS and set up an account.
Cloud Essentials 1: Learn about EC2 (virtual machines), ELB (load balancing), and EBS (storage).
Cloud Essentials 2: Explore AWS Lambda (serverless), ECS/EKS (container management).
Cloud Essentials 3: Get familiar with RDS (databases), DynamoDB (NoSQL), and S3 (storage).
ML Basics & AWS SageMaker: Introduction to machine learning concepts and using SageMaker for model building.
Data Collection & Engineering: Learn how to use Pandas for data manipulation, and AWS Glue & Athena for ETL and querying.
Visualization & Preprocessing: Use Matplotlib, Seaborn, and AWS Data Wrangler for visualization and preprocessing (feature scaling, encoding, handling missing data).
Data Preprocessing: Handle feature scaling, encoding, null values, and outliers.
Feature Selection & Train-Test Split: Learn how to select important features and split data for training/testing.
Preprocessing with SageMaker Scikit-Learn Container: Leverage SageMaker for data preparation.
Modeling & Evaluation: Build and evaluate models (classification, regression) using metrics like accuracy and precision.
SageMaker Built-in Algorithms: Use AWS SageMaker’s pre-built algorithms for faster model development.
SageMaker Canvas & Autopilot: Explore SageMaker Canvas for visual modeling and Autopilot for automated ML workflows.
RNN & NLP Basics: Introduction to Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP).
Version Control & CI/CD: Use Git, GitHub, and implement CI/CD pipelines for model deployment.
Deploying with SageMaker: Deploy machine learning models with SageMaker.
AI Services: Explore AWS AI tools like Rekognition (image analysis), Polly (text-to-speech), Lex (chatbots), and Transcribe (speech-to-text).
Looking for in-depth Syllabus Information? Explore your endless possibilities in ML Ops using AWS 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 Reports
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
How do I evaluate a machine learning model?
Model evaluation involves using performance metrics like accuracy, precision, recall, F1-score, mean squared error (MSE), and others to assess how well your model is performing on both the training and testing datasets.
What is the role of Pandas in machine learning?
Pandas is a Python library that provides powerful data structures for manipulating structured data (e.g., tables, CSVs). It’s commonly used for cleaning and preparing data before using it in machine learning models.
How do I use AWS for machine learning?
To start with AWS for machine learning, you can use services like SageMaker to handle data preprocessing, model training, and deployment. You can also use tools like AWS Glue for ETL, S3 for data storage, and EC2 for running your models.
What is EC2, and how do I use it?
EC2 (Elastic Compute Cloud) provides virtual servers (instances) in the cloud. You can launch and manage EC2 instances to run applications, host websites, or perform computations. To use it, you need to choose an instance type and configure security settings.
What is CI/CD, and why is it important in machine learning?
CI/CD (Continuous Integration/Continuous Deployment) automates the process of testing, integrating, and deploying changes to software, including machine learning models. It helps streamline workflows and ensures that models are continuously updated and deployed without manual intervention.
What is Amazon Lex, and how can I build a chatbot?
Amazon Lex is a service for building conversational interfaces (chatbots) using voice and text. It integrates with AWS Lambda and other AWS services, allowing you to create sophisticated bots with natural language understanding (NLU).
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