MLOps Training Course in Hyderabad

Duration30 Hours

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)

Introduction to MLOps Concepts

What is MLOps?
Importance of MLOps
MLOps Tools and Techniques
Course Goals and Outcomes
Tools and Technologies Overview

Python for MLOps

2.1 Fundamentals of Python for MLOps
- Key Python Libraries and Frameworks (e.g., NumPy, Pandas, Scikit-Learn)
- Python Programming Basics for ML Tasks
2.2 Streamlining ML Processes Using Python
- Automation Scripts for ML Workflows
- Data Pipelines and ETL Processes
2.3 Effective MLOps Practices with Python
- Code Organization and Maintenance
- Best Practices for Python in MLOps

Python for Data Science

3.1 Python in the Context of Data Science
- Python’s Role in Data Science
- Key Libraries (e.g., NumPy, Pandas, Matplotlib)
3.2 Data Manipulation and Analysis Techniques
- Data Cleaning and Transformation
- Exploratory Data Analysis (EDA)
3.3 Enhancing the Data Science Lifecycle with Python
- Integrating Python with Data Science Tools
- Optimizing Data Science Workflows

Model Development and Training

4.1 Building a Machine Learning Model
- Introduction to Model Development
- Common Algorithms and Techniques
4.2 Data Preprocessing and Feature Engineering
- Data Cleaning and Transformation
- Feature Selection and Engineering
4.3 Training the ML Model
- Training Strategies and Techniques
- Parameter Tuning and Optimization
4.4 Model Evaluation and Validation
- Metrics and Cross Validation Techniques
- Assessing Model Performance
4.5 Reproducible Environments
- Creating Reproducible ML Environments
- Versioning and Tracking Models
4.6 Preparing Models for Deployment
- Model Serialization
- Packaging and Exporting Models

Flask for Model Deployment

5.1 Introduction to Flask
- What is Flask?
- Benefits of Using Flask for ML Applications
5.2 Setting Up a Flask Application
- Creating a Basic Flask App
- Structuring Flask Projects
5.3 Integrating Machine Learning Models with Flask
- Loading and Using ML Models in Flask
- Creating APIs for Model Predictions
5.4 Deploying Flask Applications
- Running Flask Applications Locally
- Best Practices for Deployment

Docker for Machine Learning

6.1 Introduction to Docker
- What is Docker?
- Benefits of Containerization
6.2 Containerization Concepts
- Docker Images and Containers
- Docker Registries and Dockerfile
6.3 Docker Builds
- Building Docker Images
- Managing Docker Containers
6.4 Docker for ML Projects
- Creating Docker Images for ML
- Building and Deploying Docker Containers for ML Projects

Kubernetes Fundamentals

7.1 Introduction to Kubernetes
- What is Kubernetes?
- Key Concepts and Architecture
7.2 Core Kubernetes Components
- Pods, Services, Deployments
- ReplicaSets, ConfigMaps, Secrets
7.3 Kubernetes Storage
- Volumes, PersistentVolumes, PersistentVolumeClaims

Working with Kubernetes Kind

8.1 Introduction to Kubernetes Kind
- What is Kind?
- Use Cases and Benefits
8.2 Setting Up Kind for Local Development
- Installing Kind
- Creating Kubernetes Clusters with Kind
8.3 Deploying ML Applications with Kind
- Running ML Workflows in Kind
- Best Practices for Local Development

Kubeflow for MLOps

9.1 Introduction to Kubeflow for MLOps
- Kubeflow Components Overview
- Integrating Kubeflow into MLOps Pipelines
9.2 Installing and Setting Up Kubeflow
- Installation on Kubernetes
- Basic Configuration
9.3 Kubeflow Pipelines
- Building and Running ML Workflows
- Creating and Managing Pipelines
9.4 Hyperparameter Tuning with Katib
- Introduction to Katib
- Configuring and Running Hyperparameter Tuning Jobs
9.5 Model Serving with KFServing
- Deploying Models with KFServing
- Managing Model Endpoints
9.6 Integrating Kubeflow Components
- End to End ML Workflows
- Best Practices for Kubeflow Integration

Managing ML Experiments with MLflow

10.1 Introduction to MLflow
- Overview of MLflow and Its Components
- Installing and Setting Up MLflow
10.2 Tracking Experiments
- Logging Parameters, Metrics, and Artifacts
- Comparing and Visualizing Experiments
10.3 Managing Experiment Lifecycle
- Managing Experiment Runs
- Versioning and Tracking Models
10.4 MLflow Models
- Saving and Loading Models
- Packaging Models for Deployment
10.5 MLflow Projects
- Defining and Running MLflow Projects
- Integrating MLflow with CI/CD Pipelines

CI/CD for Machine Learning

11.1 Introduction to CI/CD
- CI/CD Concepts and Importance
- CI/CD in the Context of MLOps
11.2 Automating Development and Testing
- Automating ML Workflows
- Testing ML Models and Pipelines
11.3 Deployment of ML Models
- Automating Model Deployment
- Building Robust CI/CD Pipelines
11.4 Jenkins for MLOps
- Introduction to Jenkins
- Setting Up and Configuring Jenkins
- Automating MLOps Workflows with Jenkins
- Enhancing CI/CD with Jenkins

Continuous Monitoring with Prometheus and Grafana

12.1 Introduction to Prometheus
- What is Prometheus?
- Prometheus Concepts and Architecture
12.2 Installing and Configuring Prometheus
- Installation Steps
- Configuring Prometheus for ML Monitoring
12.3 Monitoring ML Applications
- Setting Up Metrics Collection
- Analyzing and Visualizing Metrics
12.4 Introduction to Grafana
- What is Grafana?
- Installing Grafana
- Integrating Grafana with Prometheus
12.5 Creating Dashboards and Alerting
- Creating and Configuring Dashboards in Grafana
- Setting Up Alerts for ML Applications

AWS Services for MLOps

13.1 Introduction to AWS EKS
- What is Amazon EKS?
- Benefits of Managed Kubernetes
13.2 Working with AWS ECR
- Overview of Amazon ECR
- Storing and Managing Docker Images
13.3 Deploying ML Applications on AWS EKS
- Setting Up EKS Clusters
- Deploying and Managing ML Workloads