Advanced MLOps Virtual Internship
This comprehensive MLOps virtual internship track is designed to prepare students for real-world industry roles in machine learning operations. Participants will gain hands-on experience with the full MLOps lifecycle, from model development and deployment to monitoring and optimization. Through a series of progressive modules, interns will learn to build robust, scalable, and efficient ML systems that drive business value.
Track Overview
Tasks & Milestones
Build a Scalable MLOps Pipeline for Recommendation Systems
HardCreate a production-ready MLOps pipeline for a recommendation system similar to the one used by Netflix.
Implement a Serverless MLOps Workflow for Image Classification
MediumDesign and implement a serverless MLOps workflow for an image classification model, similar to the one used by Google Cloud Vision API.
Develop a Continuous Deployment Pipeline for NLP Models
HardCreate a continuous deployment pipeline for deploying and managing natural language processing (NLP) models, similar to the one used by Amazon Comprehend.
Automated ML Workflow for Personalized Movie Recommendations
AdvancedCreate a scalable and automated ML workflow for personalized movie recommendations, similar to the system used by Netflix.
Automated ML Workflow for Predictive Maintenance in Industrial IoT
AdvancedDevelop an automated ML workflow for predictive maintenance in industrial IoT, similar to the systems used by companies like GE and Siemens.
Automated ML Workflow for Fraud Detection in Financial Services
AdvancedCreate an automated ML workflow for fraud detection in financial services, similar to the systems used by companies like Amazon and Visa.
Implement Distributed Tracing for Microservices Observability
MediumCreate a distributed tracing solution similar to what companies like Google use for end-to-end observability of their microservices architecture.
Implement Anomaly Detection for Proactive Monitoring
MediumCreate an anomaly detection system similar to what companies like Netflix use to proactively monitor and identify issues in their production environments.
Implement Observability for a Serverless Application
MediumCreate an observability solution similar to what companies like Amazon use to monitor and troubleshoot their serverless applications.
Optimize ML Model Deployment with Kubeflow
AdvancedCreate a scalable and efficient ML model deployment pipeline using Kubeflow, similar to the systems used by companies like Google and Amazon.
Implement Scalable ML Inference with AWS SageMaker
AdvancedDesign and implement a scalable and cost-effective ML inference system using AWS SageMaker, similar to the solutions used by companies like Netflix and Amazon.
Implement a Scalable and Fault-Tolerant ML Training Pipeline with Apache Airflow
AdvancedDesign and implement a scalable and fault-tolerant ML training pipeline using Apache Airflow, similar to the systems used by companies like Google and Netflix.
Implement a Scalable MLOps Pipeline for Real-Time Recommendations
AdvancedCreate a production-ready MLOps pipeline for a real-time recommendation system, similar to the one used by Netflix.
Develop a Scalable MLOps Platform for Anomaly Detection
AdvancedCreate a production-ready MLOps platform for anomaly detection, similar to the one used by Google Cloud Platform.
Implement a Distributed MLOps Pipeline for Large-Scale Image Classification
AdvancedCreate a production-ready MLOps pipeline for a large-scale image classification system, similar to the one used by Amazon Web Services.
Prerequisites
- • Python programming
- • Machine learning fundamentals
- • Cloud computing concepts
- • Familiarity with Git and version control
- • Basic understanding of software engineering practices
Certificate
Certificate of Completion
Earn a certificate upon successful completion