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Free Download Complete Roadmap To Becoming An Mlops Engineer Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.46 GB | Duration: 2h 59m Master the MLOps Maze: Unlock the Secrets with the Right Tools! What you'll learn MLOps Lifecycle Mastery: Understand the complete MLOps process from data preparation to model deployment and monitoring. Version Control Tools: Learn essential tools for code (Git), data (DVC), and model versioning (MLflow). Core Skills and Frameworks: Gain proficiency in key languages (Python, Bash) and ML frameworks (TensorFlow, PyTorch). Cloud and Deployment: Deploy ML models on AWS using Docker, Kubernetes, and CI/CD pipelines. Monitoring and Maintenance: Monitor, manage, and maintain models in production with tools like Prometheus and Grafana. Requirements No prerequisites required! This course is designed for beginners and intermediate ML engineers. All you need is a willingness to learn and explore the field of MLOps. Description Welcome to "Complete Roadmap to Becoming an MLOps Engineer" - your ultimate guide to navigating the ever-evolving world of MLOps!In this course, we break down the complex roadmap of MLOps into simple, digestible steps, ensuring that you're equipped with the right tools to streamline your machine learning workflows and thrive in the industry. Whether you're just starting out or have some experience, this course is designed to empower you with the skills to implement scalable machine learning models in production.Why MLOps?As AI and ML are increasingly becoming integral to business operations, managing and deploying models at scale can be challenging. This is where MLOps comes in-helping bridge the gap between data science and operations. MLOps ensures that machine learning models are efficiently developed, deployed, monitored, and maintained in a reliable, automated, and scalable manner.What Will You Learn?Fundamentals of MLOps: Understand the core principles that differentiate MLOps from traditional DevOps and why it's crucial for successful AI implementations.End-to-End Machine Learning Pipeline: Learn how to build and manage the entire lifecycle of machine learning models-from model development to deployment and monitoring.Version Control for Code, Data, and Models: Discover the best practices for tracking and versioning everything in your ML workflows, ensuring reproducibility and scalability.Continuous Integration/Continuous Deployment (CI/CD) for ML: Automate your machine learning workflows with CI/CD pipelines, and understand how to apply DevOps practices to machine learning.Model Monitoring & Retraining: Explore how to monitor models in production, track performance, and implement retraining mechanisms to ensure accuracy over time.Containerization with Docker: Master the use of Docker to create portable, reliable, and consistent environments for your ML models across platforms.Cloud & Deployment Strategies: Learn how to deploy models in real-world environments using cloud services (like AWS, GCP, or Azure) and container orchestration systems like Kubernetes.MLOps Best Practices and Tools: Get hands-on with essential MLOps tools like MLflow, Kubeflow, DVC, and more to manage the lifecycle of your models and ensure smooth collaboration between data scientists and engineers.Who Should Enroll?Aspiring MLOps Engineers: If you're looking to transition from a traditional ML or data science role to an MLOps-focused position, this course will give you the skills and insights you need.Data Scientists and ML Engineers: If you want to learn how to scale your models from development to production while mastering automation and lifecycle management.DevOps Engineers: If you're interested in expanding your skill set to support machine learning model deployments, monitoring, and infrastructure management.Students & Enthusiasts: Even if you're just getting started in the world of machine learning and AI, this course will provide a strong foundation for learning how to integrate operations with ML. Overview Section 1: Introduction to MLOps Lecture 1 Introduction Lecture 2 What is MLOps ? Lecture 3 Why is MLOps required ? Lecture 4 Resources Section 2: Understanding Stages of MLOps Lecture 5 Problem Definition, Data Collection and Processing Lecture 6 Metrics Definition, Data Analysis and Feature Engineering Lecture 7 Model Training, Deployment and Monitoring Section 3: Building base for MLOps Lecture 8 Basics Tools and Skillset for MLOps- Part 1 Lecture 9 Basics Tools and Skillset for MLOps- Part2 Section 4: Tools and skills for Problem Definition Lecture 10 Stage 1: Problem Definition Section 5: Tools and skills for Data Collection Lecture 11 Stage 2: Data Collection Lecture 12 Data Collection Part 2 Section 6: Tools and Skills for Data pre-processing and Storage Lecture 13 Stage 3: Data Pre-processing Section 7: Tools and Skills for Metrics Definition Lecture 14 Stage 4: Metrics Definition Section 8: Tools and Skills for Data Exploration and Analysis Lecture 15 Stage 5: Data Exploration and Analysis Section 9: Tools and Skills for Feature Extraction and Engineering Lecture 16 Stage 6: Feature Extraction and Engineering Section 10: Tools and Skills for Model Training Lecture 17 Stage 7: Model Training Section 11: Tools and Skills for Model Deployment and Integration Lecture 18 Stage 8: Model Deployment Section 12: Tools and Skills for Model Monitoring Lecture 19 Stage 9: Model Monitoring Lecture 20 Congratulation! What Next ? This course is perfect for aspiring ML engineers who want to specialize in MLOps, data scientists eager to expand their expertise into machine learning operations, and software engineers interested in building and deploying ML models. Whether you're a beginner or have some experience in the field, this course will guide you on your path to becoming an MLOps expert. It's also ideal for anyone passionate about mastering the end-to-end process of machine learning, from development to deployment and beyond. If you're ready to dive deep into the world of MLOps and take your career to the next level, this course is for you! 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Free Download Complete Mlops Bootcamp With 10+ End To End Ml Projects Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 50.09 GB | Duration: 47h 39m End-to-End MLOps Bootcamp: Build, Deploy, and Automate ML with Data Science Projects What you'll learn Build scalable MLOps pipelines with Git, Docker, and CI/CD integration. Implement MLFlow and DVC for model versioning and experiment tracking. Deploy end-to-end ML models with AWS SageMaker and Huggingface. Automate ETL pipelines and ML workflows using Apache Airflow and Astro. Monitor ML systems using Grafana and PostgreSQL for real-time insights. Requirements Basic understanding of Python programming. Familiarity with machine learning concepts and algorithms. Basic knowledge of Git and GitHub for version control. Understanding of Docker for containerization (optional but helpful). Awareness of cloud computing concepts (AWS preferred, but not mandatory). Description Welcome to the Complete MLOps Bootcamp With End to End Data Science Project, your one-stop guide to mastering MLOps from scratch! This course is designed to equip you with the skills and knowledge necessary to implement and automate the deployment, monitoring, and scaling of machine learning models using the latest MLOps tools and frameworks.In today's world, simply building machine learning models is not enough. To succeed as a data scientist, machine learning engineer, or DevOps professional, you need to understand how to take your models from development to production while ensuring scalability, reliability, and continuous monitoring. This is where MLOps (Machine Learning Operations) comes into play, combining the best practices of DevOps and ML model lifecycle management.This bootcamp will not only introduce you to the concepts of MLOps but will take you through real-world, hands-on data science projects. By the end of the course, you will be able to confidently build, deploy, and manage machine learning pipelines in production environments.What You'll Learn:Python Prerequisites: Brush up on essential Python programming skills needed for building data science and MLOps pipelines.Version Control with Git & GitHub: Understand how to manage code and collaborate on machine learning projects using Git and GitHub.Docker & Containerization: Learn the fundamentals of Docker and how to containerize your ML models for easy and scalable deployment.MLflow for Experiment Tracking: Master the use of MLFlow to track experiments, manage models, and seamlessly integrate with AWS Cloud for model management and deployment.DVC for Data Versioning: Learn Data Version Control (DVC) to manage datasets, models, and versioning efficiently, ensuring reproducibility in your ML pipelines.DagsHub for Collaborative MLOps: Utilize DagsHub for integrated tracking of your code, data, and ML experiments using Git and DVC.Apache Airflow with Astro: Automate and orchestrate your ML workflows using Airflow with Astronomer, ensuring your pipelines run seamlessly.CI/CD Pipeline with GitHub Actions: Implement a continuous integration/continuous deployment (CI/CD) pipeline to automate testing, model deployment, and updates.ETL Pipeline Implementation: Build and deploy complete ETL (Extract, Transform, Load) pipelines using Apache Airflow, integrating data sources for machine learning models.End-to-End Machine Learning Project: Walk through a full ML project from data collection to deployment, ensuring you understand how to apply MLOps in practice.End-to-End NLP Project with Huggingface: Work on a real-world NLP project, learning how to deploy and monitor transformer models using Huggingface tools.AWS SageMaker for ML Deployment: Learn how to deploy, scale, and monitor your models on AWS SageMaker, integrating seamlessly with other AWS services.Gen AI with AWS Cloud: Explore Generative AI techniques and learn how to deploy these models using AWS cloud infrastructure.Monitoring with Grafana & PostgreSQL: Monitor the performance of your models and pipelines using Grafana dashboards connected to PostgreSQL for real-time insights.Who is this Course For?Data Scientists and Machine Learning Engineers aiming to scale their ML models and automate deployments.DevOps professionals looking to integrate machine learning pipelines into production environments.Software Engineers transitioning into the MLOps domain.IT professionals interested in end-to-end deployment of machine learning models with real-world data science projects.Why Enroll?By enrolling in this course, you will gain hands-on experience with cutting-edge tools and techniques used in the industry today. Whether you're a data science professional or a beginner looking to expand your skill set, this course will guide you through real-world projects, ensuring you gain the practical knowledge needed to implement MLOps workflows successfully.Enroll now and take your data science skills to the next level with MLOps! Overview Section 1: Introduction Lecture 1 Introduction Section 2: IDE's And Code Editors You Can Use Lecture 2 Getting Started With Google Colab Lecture 3 Getting Started With Github Codespace Lecture 4 Anaconda And VS Code Installation Section 3: Python Prerequisites Lecture 5 Getting Started With VS Code And Environment Lecture 6 Python Basics-Syntax and Semantics Lecture 7 Variables In Python Lecture 8 Basics Data Types Lecture 9 Operators In Python Lecture 10 Conditional Statements In Python Lecture 11 Loops In Python Lecture 12 Practical Examples Of List Lecture 13 Sets In Python Lecture 14 Tuples In Python Lecture 15 Dictionaries In Python Lecture 16 Functions In Python Lecture 17 Python Function Examples Lecture 18 Lambda Functions In Python Lecture 19 Map functions In Python Lecture 20 Python Filter Function Lecture 21 Import Modules And Packages In Python Lecture 22 Standard Library Overview Lecture 23 File Operation In Python Lecture 24 Working With File Paths Lecture 25 Exception Handling In Python Lecture 26 OOPS In Python Lecture 27 Inheritance In Python Lecture 28 Polymorphism In Python Lecture 29 Encapsulation In Python Lecture 30 Abstraction In Python Lecture 31 Magic Methods In Python Lecture 32 Custom Exception In Python Lecture 33 Operator OverLoading In Python Lecture 34 Iterators In Python Lecture 35 Generators In Python Lecture 36 Decorators In Python Lecture 37 Working With Numpy In Python Lecture 38 Pandas DataFrame And Series Lecture 39 Data Manipulation And Analysis Lecture 40 Data Source Reading Lecture 41 Logging In Python Lecture 42 Logging With Multiple Loggers Lecture 43 Logging In a Real World Examples Section 4: Complete Flask Tutorial Lecture 44 Introduction To Flask Framework Lecture 45 Understanding A Sample Flask Application Lecture 46 Integrating HTML With Flask Framework Lecture 47 HTTP Verbs Get And Post Lecture 48 Building Dynamic Url With Jinja 2 Lecture 49 Put Delete And API's In Flask Section 5: Git and Github Lecture 50 Getting Started With Git And Github Lecture 51 Part 2- Git Merge,Push, Checkout And Log With Commands Lecture 52 Part 3- Resolving Git Branch Merge Conflict Section 6: Complete MLFLOW Tutorials Lecture 53 Introduction To MLFLOW Lecture 54 Getting Started With MLFLOW Lecture 55 Creating MLFLOW Environment Lecture 56 Getting Started With MLFLow Tracking Server Lecture 57 Deep Diving Into MLFlow Experiments Lecture 58 Getting Started With MLFlow ML Project Lecture 59 First ML Project With MLFLOW Lecture 60 Inferencing Model Artifacts With MLFlow Inferencing Lecture 61 MLFLOW Model Registry Tracking Section 7: ML Project Integration With MLFLOW Tracking Lecture 62 Data Preparation House Price Prediction Lecture 63 Model Building And MLFLOW Tracking Section 8: Deep Learning ANN Model Building Integration With MLFLOW Lecture 64 ANN With MLFLOW- Part 1 Lecture 65 ANN with MLFLOW-Part 2 Section 9: Getting Started With DVC- Data Version Control Lecture 66 Introduction To DVC With Practical Implementation Section 10: Getting Started With Dagshub Lecture 67 Introduction To Dagshub Remote Repository Lecture 68 Creating First Remote Repo Using Dagshub Lecture 69 DVC With Dagshub Remote Repository Section 11: End To End Machine Learning Pipeline Using GIT, DVC,MLFLOW And DAGSHUB Lecture 70 Getting Started With Project Structure Lecture 71 Implemeting Data Preprocessing Pipeline Lecture 72 Implementing Model Training Pipeline with MLFLOW Setup Lecture 73 MLFLOW Experiment Tracking In Dagshub Lecture 74 ML Evaluation Piepline With MLFLOW Lecture 75 Run The Complete Pipeline With DVC Stage And Repro Section 12: MLFLOW With AWS Cloud Lecture 76 Introduction To MLFLOW In AWS Lecture 77 MLFLOW Project Set Up With Installation Lecture 78 Implementing The End To End Project With MLFLOW Lecture 79 AWS Cloud EC2,IAM,S3 Bucket Set Up Lecture 80 AWS EC2 Instance- Setting MLFLOW Tracking Server Section 13: Complete Basic To Advance Dockers Lecture 81 Introduction To Docker Series Lecture 82 What are Dockers And Containers Lecture 83 Docker Images vs Containers Lecture 84 Dockers vs Virtual Machines Lecture 85 Dockers Installation Lecture 86 Creating A Docker Image Lecture 87 Docker Basic Commands Lecture 88 Push Docker Image To Docker Hub Lecture 89 Docker Compose Section 14: Getting Started With Airflow Lecture 90 Introduction To Apache Airflow Lecture 91 Key Components Of Apache Airflow Lecture 92 Why Airflow For MLOPS Lecture 93 Setting Up Airflow With Astro Lecture 94 Building Your First DAG With Airflow Lecture 95 Designing Mathematical Calculation DAG With Airflow Lecture 96 Getting Started With TaskFlow API Using Apache Airflow Section 15: Airflow ETL Pipeline with Postgres and API Integration In ASTRO Cloud And AWS Lecture 97 Introduction To ETL Pipeline Lecture 98 ETL Problem Statement And Project Structure Set Up Lecture 99 Defining ETL DAG With Implementing Steps Lecture 100 Step 1- Setting Up Postgres And Creating Table Task In Postgres Lecture 101 Step 2- NASA API Integration With Extract Pipeline Lecture 102 Step 3- Building Transformation And Load Pipeline Lecture 103 ETL Pipeline Final Implementation With AirFlow Connection Set Up Lecture 104 ETL Pipeline Deployment In Astro Cloud And AWS Section 16: Introduction To Github Actions Lecture 105 What is Github Action and CI CD Pipeline Lecture 106 What is Developers Workflow With Examples Lecture 107 Practicals-Automate Testing Workflow With Python Section 17: End To End Github Action Workflow Project With Dockerhub Lecture 108 Github Action Workflow Project with Docker hub Lecture 109 Setting Project Structure With Github Repo Lecture 110 Setting Up Github Repository Lecture 111 Implementing Project With Flask And Dockers Lecture 112 Building the Yaml file for Dockers Section 18: Getting Started With Your First End To End Data Science Project With Deployment Lecture 113 Project Structure, Github Repo And Environment Set Up Lecture 114 Custom Logging Implementation Lecture 115 Common Utilities Functions Implementation Lecture 116 Step By Step Building Data Ingestion Pipeline- Part 1 Lecture 117 Data Ingestion Pipeline-Part 2 Lecture 118 Complete Data Validation Pipeline Implementation Lecture 119 Complete Data Transformation Pipeline Implementation Lecture 120 Model Trainer Pipeline Implementation Lecture 121 Model Evaluation Pipeline Implementation Lecture 122 Training And Prediction Pipeline With Flask App Section 19: End To End MLOPS Projects With ETL Pipelines- Building Network Security System Lecture 123 Project Structure Set up With Environment Lecture 124 Github Repository Set Up With VS Code Lecture 125 Packaging the Project With Setup.py Lecture 126 Logging And Exception Handling Implementation Lecture 127 Introduction To ETL Pipelines Lecture 128 Setting Up MongoDb Atlas Lecture 129 ETL Pipeline Setup With Python Lecture 130 Data Ingestion Architecture Lecture 131 Implementing Data Ingestion Configuration Lecture 132 Implementing Data Ingestions Component Lecture 133 Implementing Data Validation-Part 1 Lecture 134 Implementing Data Validation- Part 2 Lecture 135 Data Transformation Architecture Lecture 136 Data Transformation Implementation Lecture 137 Model Trainer-Part 1 Lecture 138 Model Trainer And Evaluation With Hyperparameter Tuning Lecture 139 Model Experiment Tracker With MLFlow Lecture 140 MLFLOW Experiment Tracking With Remote Respository Dagshub Lecture 141 Model Pusher Implementation Lecture 142 Model Training Pipeline Implementation Lecture 143 Batch Prediction Pipeline Implementation Lecture 144 Final Model And Artifacts Pusher To AWS S3 buckets Lecture 145 Building Docker Image And Github Actions Lecture 146 Github Action-Docker Image Push to AWS ECR Repo Implementation Lecture 147 Final Deployment To EC2 instance Section 20: End To End DS Project Implementation With Mulitple AWS,Azure Deployment Lecture 148 Github And Code Setup Lecture 149 Project structure Logging And Exception Lecture 150 Project Problem Statement EDA And Model Training Lecture 151 Data Ingestion Implementation Lecture 152 Data Transformation Implementation Lecture 153 Model Trainer Implementation Lecture 154 Hyperparameter Tuning Implementation Lecture 155 Building Prediction Pipeline Lecture 156 Deployment AWS Beanstalk Lecture 157 Deployment In EC2 Instance Lecture 158 Deployment In Azure Web App Section 21: Build, Train ,Deploy And Create Endpoints For ML Project Using AWS Sagemaker Lecture 159 Introduction To AWS Sagemaker Amd Project Set up Lecture 160 EDA,AWS IAM, S3 Set up With Data Ingestion Lecture 161 Implementing Training Script For AWS Sagemaker Lecture 162 Training With An On Spot Instance In AWS Sagemaker Lecture 163 Deployment Of Endpoint With AWS Sagemaker And Inferencing Section 22: Grafana-Open Source Tool For Data Visualization And Monitoring Lecture 164 Introduction To Grafana Open Source Tool Lecture 165 Grafana Cloud Set Up And Problem Statement Lecture 166 Visualization Implementation With Grafana Cloud And Postgresql In AWS Section 23: Generative AI Series With AWS LLMOPS Lecture 167 LifeCycle Of Gen AI Projects In Cloud Lecture 168 Blog Generation Generative AI App Using AWS Lambda And Bedrock Lecture 169 Deployment Of HuggingFace LLM Model In AWS Sagemaker Lecture 170 End To End GENAI App Using NVIDIA NIM Data Scientists and Machine Learning Engineers looking to scale and deploy ML models.,DevOps professionals wanting to integrate ML pipelines.,Software Engineers interested in transitioning to MLOps.,Beginners with basic ML knowledge aiming to learn end-to-end deployment.,IT professionals eager to understand MLOps tools and practices for real-world projects. 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