MLOps Certified Professional (MLOCP)

Introduction to MLOps

• Understanding MLOps and its Role in Machine Learning

• Overview of MLOps Life Cycle

Key Principles and Best Practices

• Automation and Continuous Integration

• Version Control for Models and Data

• Collaboration between Data Scientists and Operations Teams

MLOps Pipeline Overview

• Data Collection, Cleaning, and Transformation

• Model Training, Testing, and Validation

• Model Deployment, Monitoring, and Maintenance

Challenges in MLOps

• Handling Model Drift

• Data and Model Governance

• Scaling Machine Learning Models

Introduction to Linux for MLOps

• Basic Linux Commands and File System Navigation

• Directory Structure and Permissions in Linux

Writing Bash Scripts for Automation

• Scripting with Shell Commands

• Creating, Debugging, and Executing Bash Scripts

Automating Common MLOps Tasks

• Automating Model Training and Evaluation

• Automating Data Pipeline Tasks with Bash

• Scheduling Tasks with Cron Jobs

Best Practices for Bash Scripting

• Writing Readable and Maintainable Scripts

• Error Handling and Debugging in Bash

Introduction to AWS for MLOps

• Overview of AWS Cloud Services

• Key AWS Components for Machine Learning

Setting up AWS for Model Deployment

• Configuring EC2, S3, Lambda, and IAM for Model Deployment

• Using AWS SageMaker for Model Training and Deployment

Scalable Infrastructure with AWS

• Autoscaling, Load Balancing, and Distributed Computing

• Serverless Architectures with AWS Lambda

Security and Compliance on AWS

• Best Practices for AWS Security

• Managing User Roles and Access Control

Introduction to Docker for MLOps

• Understanding Docker and Containerization

• Benefits of Using Docker in MLOps

Creating and Managing Docker Containers

• Writing Dockerfiles and Building Containers

• Running Containers Locally and in the Cloud

Docker for Model Packaging and Deployment

• Packaging ML Models into Docker Containers

• Containerizing Dependencies and Code

Managing Docker Containers

• Docker Compose for Multi-Container Applications

• Managing Docker Containers in the Cloud

Agile Project Management with Jira

• Setting up and Managing Projects in Jira

• Managing Tasks, Sprints, and Backlogs

Tracking and Reporting in Jira

• Creating Reports and Dashboards

• Integrating Jira with Other MLOps Tools

Documenting MLOps Workflows in Confluence

• Writing Detailed Documentation for Data and Model Pipelines

• Collaborative Documentation for Team Members

Best Practices for Using Jira and Confluence

• Managing Workflow with Automation Rules

• Creating Actionable Roadmaps and Milestones

Building Backend APIs with Python and Flask

• Setting up a Flask Project for MLOps Applications

• Writing RESTful APIs for Model Deployment

Integrating MySQL Database with Flask

• Setting Up MySQL for Storing Data and Model Results

• Writing Queries and Handling Transactions

Connecting Flask with Front-End for MLOps

• Serving Machine Learning Predictions via APIs

• Integrating Flask with Web Front-End for Model Results

Testing and Debugging Flask Applications

• Unit Testing with Pytest for Flask APIs

• Debugging and Improving API Performance

Introduction to Git and GitHub

• Understanding Git Version Control System

• Key Concepts: Commits, Branches, Merges, and Repositories

Collaborating with GitHub

• Setting up and Managing Repositories on GitHub

• Pull Requests, Merging, and Conflict Resolution

Best Practices for Version Control in MLOps

• Managing Code for Multiple ML Models

• Tracking Changes in Data and Models

Advanced Git Techniques

• Handling Large Files with Git LFS

• Managing Submodules in Git Repositories

Introduction to Kubernetes for MLOps

• Overview of Kubernetes Architecture

• Managing Containers with Kubernetes

Deploying Applications on Kubernetes

• Setting up Kubernetes Cluster for ML Applications

• Creating and Managing Pods, Deployments, and Services

Using Helm for Kubernetes Management

• Introduction to Helm Charts

• Deploying Applications with Helm

Best Practices for Kubernetes in MLOps

• Scaling and Auto-Scaling ML Models

• Monitoring Kubernetes Clusters for ML Applications

Introduction to Infrastructure as Code (IaC)

• Overview of Terraform and Its Benefits

• Writing Infrastructure Code with Terraform

Provisioning Cloud Resources with Terraform

• Managing AWS Services with Terraform (S3, EC2, IAM, etc.)

• Automating Cloud Infrastructure Provisioning for MLOps

Managing and Updating Infrastructure with Terraform

• Managing Versioning and State Files in Terraform

• Updating Cloud Infrastructure with Terraform Modules

Introduction to Continuous Integration and ArgoCD

• What is Continuous Integration (CI)?

• Overview of ArgoCD for GitOps

Setting up CI Pipelines with ArgoCD

• Automating Model Deployment with ArgoCD

• Integrating ArgoCD with GitHub for Continuous Delivery

Managing Deployments with ArgoCD

• Rolling Back and Updating Deployments in Kubernetes

• Monitoring Deployment Status with ArgoCD

Introduction to Monitoring with Prometheus

• Setting up Prometheus for Metrics Collection

• Defining and Querying Metrics in Prometheus

Visualizing Metrics with Grafana

• Setting up Dashboards in Grafana

• Integrating Prometheus with Grafana for Visualization

Using Prometheus and Grafana for MLOps

• Monitoring Machine Learning Model Performance

• Alerting for Model Drift or Anomalies

Introduction to Kubeflow for Model Packaging

• What is Kubeflow and Its Role in MLOps

• Packaging Models with Kubeflow Pipelines

Model Versioning with Kubeflow

• Tracking Model Versions and Artifacts in Kubeflow

• Reproducibility of Models and Experiments

Introduction to MLflow

• Overview of MLflow and its Key Components

• Packaging Models with MLflow for Deployment

Tracking Experiments with MLflow

• Recording and Comparing Model Runs

• Organizing and Versioning Models with MLflow

MLflow for Model Deployment

• Using MLflow for Serving Models

• Integrating MLflow with Production Environments

Introduction to Jupyter Notebooks

• Writing and Running Machine Learning Code in Jupyter

• Visualizing Data and Results in Jupyter Notebooks

Using Jupyter Notebooks for Experimentation

• Documenting Machine Learning Models and Experiments

• Sharing Notebooks with the Team for Collaboration

Introduction to TensorFlow

• Understanding TensorFlow’s Role in Deep Learning

• Building and Training Neural Networks with TensorFlow

Optimizing Models in TensorFlow

• Using TensorFlow’s Keras API for Model Building

• Hyperparameter Tuning and Optimization in TensorFlow

Introduction to PyTorch

• Basics of PyTorch for Deep Learning Models

• Building Neural Networks with PyTorch

Model Training and Optimization with PyTorch

• Training Models with PyTorch

• PyTorch’s Autograd System for Differentiation

Introduction to Pytest

• Writing Unit Tests for Machine Learning Models

• Testing Model Functions and Code with Pytest

Automated Model Validation

• Setting Up Pytest for Continuous Model Validation

• Validating Model Outputs and Edge Cases

Overview of scikit-learn for Testing Models

• Using scikit-learn for Model Evaluation and Metrics

• Cross-Validation Techniques in scikit-learn

Model Validation and Testing Best Practices

• Hyperparameter Tuning with scikit-learn

• Validating ML Models with Metrics and Visualizations

Introduction to KServe (KFServing)

• What is KServe and Its Role in MLOps

• Serving Machine Learning Models with KServe

Managing Model Inference with KServe

• Setting up KServe for Auto-Scaling and Model Management

• Rolling Back and Updating Models in Production

Introduction to Apache Airflow

• Overview of Data Pipelines and Workflow Automation

• Setting up Apache Airflow for ML Data Pipelines

Managing Complex Workflows with Airflow

• Automating ETL Jobs and ML Training Pipelines

• Scheduling and Monitoring Workflows in Airflow

Experiment Tracking with MLflow

• Recording and Managing ML Experiments

• Tracking Hyperparameters, Metrics, and Outputs

Reproducing and Comparing Experiments

• Comparing Multiple Experiment Runs in MLflow

• Managing Experiment Artifacts for Future Use