2024 Deploy ML Model in Production with FastAPI and Docker | Udemy [Update 08/2024]
English | Size: 14.24 GB
Genre: eLearning[/center]
Deploy ML Model with ViT, BERT and TinyBERT HuggingFace Transformers with Streamlit, FastAPI and Docker at AWS
What you'll learn
Deploy Machine Learning Models with FastAPI: Learn to build and deploy RESTful APIs for serving ML models efficiently.
Master Cloud-Based ML Deployments with AWS: Gain hands-on experience deploying, managing, and scaling ML models on AWS EC2 and S3.
Automate ML Operations with Boto3 and Python: Automate cloud tasks like instance creation, data storage, and security configuration using Boto3.
Containerize ML Applications Using Docker: Build and manage Docker containers to ensure consistent and scalable ML deployments across environments.
Streamline Model Inference with Real-Time APIs: Develop high-performance APIs that deliver fast and accurate predictions for production-grade applications.
Optimize Machine Learning Pipelines for Production: Design and implement end-to-end ML pipelines, from data ingestion to model deployment, using best practices.
Implement Secure and Scalable ML Infrastructure: Learn to integrate security protocols and scalability features into your cloud-based ML deployments.
Create Interactive Web Apps with Streamlit: Build and deploy interactive ML-powered web applications that are accessible and user-friendly.
Deploy Transformers for NLP and Computer Vision: Fine-tune and deploy TinyBERT and Vision Transformers for sentiment analysis, disaster tweets, and images.
Monitor and Maintain ML Models in Production: Implement monitoring, A/B testing, and bias detection to ensure your models remain reliable and effective in prod.
Welcome to Production-Grade ML Model Deployment with FastAPI, AWS, Docker, and NGINX!
Unlock the power of seamless ML model deployment with our comprehensive course, Production-Grade ML Model Deployment with FastAPI, AWS, Docker, and NGINX. This course is designed for data scientists, machine learning engineers, and cloud practitioners who are ready to take their models from development to production. You'll gain the skills needed to deploy, scale, and manage your machine learning models in real-world environments, ensuring they are robust, scalable, and secure.
What You Will Learn:
Streamline ML Operations with FastAPI: Master the art of serving machine learning models using FastAPI, one of the fastest-growing web frameworks. Learn to build robust RESTful APIs that facilitate quick and efficient model inference, ensuring your ML solutions are both accessible and scalable.
Harness the Power of AWS for Scalable Deployments: Leverage AWS services like EC2, S3, ECR, and Fargate to deploy and manage your ML models in the cloud. Gain hands-on experience automating deployments with Boto3, integrating models with AWS infrastructure, and ensuring they are secure, reliable, and cost-efficient.
Containerize Your Applications with Docker: Discover the flexibility of Docker to containerize your ML applications. Learn how to build, deploy, and manage Docker containers, ensuring your models run consistently across different environments, from development to production.
Build and Deploy End-to-End ML Pipelines: Understand the intricacies of ML Ops by constructing end-to-end machine learning pipelines. Explore data management, model monitoring, A/B testing, and more, ensuring your models perform optimally at every stage of the lifecycle.
Automate Deployments with Boto3: Automate the deployment of your ML models using Python and Boto3. From launching EC2 instances to managing S3 buckets, streamline cloud operations, making your deployments faster and more efficient.
Scale ML Models with NGINX: Learn to use NGINX with Docker-Compose to scale your ML applications across multiple instances, ensuring high availability and performance in production.
Deploy Serverless ML Models with AWS Fargate: Dive into serverless deployment using AWS Fargate, and learn how to package, deploy, and manage ML models with AWS ECR and ECS for scalable, serverless applications.
Real-World ML Use Cases: Apply your knowledge to real-world scenarios by deploying models for sentiment analysis, disaster tweet classification, and human pose estimation. Using cutting-edge transformers and computer vision techniques, you'll gain practical experience in bringing AI to life.
Deploy Interactive ML Applications with Streamlit: Create and deploy interactive web applications using Streamlit. Integrate your FastAPI-powered models into user-friendly interfaces, making your ML solutions accessible to non-technical users.
Monitor and Optimize Production ML Models: Implement load testing, monitoring, and performance optimization techniques to ensure your models remain reliable and efficient in production environments.
Why This Course?
In today's fast-paced tech landscape, the ability to deploy machine learning models into production is a highly sought-after skill. This course combines the latest technologies-FastAPI, AWS, Docker, NGINX, and Streamlit-into one powerful learning journey. Whether you're looking to advance your career or enhance your skill set, this course provides everything you need to deploy, scale, and manage production-grade ML models with confidence.
By the end of this course, you'll have the expertise to deploy machine learning models that are not only effective but also scalable, secure, and ready for production in real-world environments. Join us and take the next step in your machine-learning journey!
Who this course is for:
Machine learning engineers who want to gain hands-on experience in setting up and configuring an end-to-end machine learning production pipeline.
Data Scientists and Machine Learning Engineers: Professionals looking to advance their skills in deploying machine learning models in production environments using FastAPI, AWS, and Docker.
Cloud Engineers and DevOps Professionals: Individuals who want to master cloud-based deployments, automate ML pipelines, and manage scalable infrastructure on AWS.
Software Developers and Engineers: Developers interested in integrating machine learning models into applications and services, with a focus on API development and containerization.
AI Enthusiasts and Practitioners: Anyone passionate about AI and machine learning who wants to gain hands-on experience in taking models from development to deployment.
Tech Professionals Transitioning into ML Ops: IT professionals or developers transitioning into machine learning operations (ML Ops) who need practical knowledge of production-grade deployment and automation tools.
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