MLOps Training Course in Hyderabad

Duration45 Days

Mode of TrainingOnline

LevelAdvanced

MLOps Course Overview

The MLOps stands for Machine Learning Operations. MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them. This MLOps course teaches how to design an ML production system end-to-end build data pipelines, establish a model baseline, develop and deploy ML models, and continuously monitor a production system.Training by Realtime Experts. Enroll Now for FREE DEMO..!

What is MLOps tools?

The MLOps tools are designed to integrate seamlessly with existing data science workflows. They typically support various data science tools and platforms, provide APIs for integration, and offer plugins or extensions for popular data science environments.

Training by Realtime Expert trainer
Live Online Classes
Recorded Videos will be Provided
Online virtual Classes available in morning, evening and weekend

You may also see course CurriculumMLOps Course Curriculum (Machine Learning Operations)

Python for MLOps

Dive into the fundamentals of Python tailored specifically for MLOps
Explore Python's role in streamlining and enhancing Machine Learning processes
Develop proficiency in leveraging Python for effective MLOps practices

Python for Data Science

Uncover the power of Python in the context of Data Science
Learn essential data manipulation and analysis techniques using Python
Understand how Python enhances the entire data science lifecycle

Git and GitHub Fundamentals

Master the essentials of version control with Git
Understand how GitHub facilitates collaborative development in MLOps
Learn to manage and track changes effectively within MLOps projects

Packaging the ML Models

Delve into the art of packaging Machine Learning models
Explore different packaging techniques and their implications
Ensure your ML models are easily deployable and reproducible

MLflow - Manage ML Experiments

Learn to effectively manage and track Machine Learning experiments
Understand the features and benefits of MLflow for experiment tracking and management
Implement MLflow in your MLOps projects for enhanced experimentation

Crash Course on YAML

Acquire a solid foundation in YAML, a key configuration language
Learn how YAML is used in MLOps for configuration and deployment
Gain practical skills in writing and interpreting YAML files

Docker for Machine Learning

Explore Docker and its role in containerizing Machine Learning applications
Understand the advantages of containerization for MLOps
Learn to build and deploy Docker containers for Machine Learning projects

Build MLApps using FastAPI

Dive into FastAPI, a modern, fast web framework for building APIs
Learn to develop ML applications using FastAPI for efficient and scalable deployments
Implement best practices for building robust MLApps

Build MLApps using Streamlit

Explore Streamlit, a powerful framework for creating interactive web applications
Develop hands-on experience in building MLApps with Streamlit
Understand how Streamlit enhances the user interface for Machine Learning applications

Build MLApps using Flask

Gain proficiency in Flask, a popular web framework for Python
Learn to build and deploy Machine Learning applications using Flask
Understand the integration of Flask with MLOps workflows

CI/CD for Machine Learning

Explore Continuous Integration and Continuous Deployment (CI/CD) pipelines in the context of MLOps
Implement automation to streamline the development, testing, and deployment of ML models
Learn to build robust CI/CD workflows for Machine Learning projects

Linux Operating System for DevOps and Data Scientists

Understand the fundamentals of the Linux operating system
Explore how Linux is essential for both DevOps and Data Scientists in MLOps
Gain practical skills in working with Linux for MLOps tasks

Working with Jenkins

Dive into Jenkins, an open-source automation server
Learn to set up and configure Jenkins for automating MLOps workflows
Understand how Jenkins enhances the efficiency of continuous integration and deployment in MLOps

Monitoring and Debugging of ML System

Gain insights into effective monitoring and debugging strategies for MLOps
Learn tools and techniques to identify and address issues in Machine Learning systems
Implement best practices for maintaining the health and performance of ML systems

Continuous Monitoring with Prometheus

Explore Prometheus, an open-source monitoring and alerting toolkit
Learn to set up continuous monitoring for MLOps using Prometheus
Understand how Prometheus enhances observability in Machine Learning applications

Deploy Applications with Docker Compose

Extend your Docker skills by mastering Docker Compose
Learn to deploy multi-container applications seamlessly using Docker Compose
Understand how Docker Compose enhances the deployment of complex MLOps architectures

Continuous Monitoring of Machine Learning Application

Dive into continuous monitoring practices specifically tailored for Machine Learning applications
Explore tools and strategies to ensure ongoing performance monitoring in MLOps
Implement solutions for proactively addressing issues in production ML systems

Monitor the ML System with WhyLogs

Explore WhyLogs, a data logging library for Machine Learning
Learn how WhyLogs facilitates efficient monitoring and logging of ML data
Implement WhyLogs to enhance the observability and traceability of your ML system

Post Productionizing ML Models

Understand the crucial steps involved in post-productionizing Machine Learning models
Explore strategies for maintaining and updating ML models in a production environment
Gain insights into best practices for ensuring the long-term success of deployed ML systems