Mlops Trainer

**MLOps Curriculum**

**Total - 40 hrs**

**SESSION - 1 (Beginner) - 4 hrs**

**Introduction to MLOps**:

- What is MLOps?
- Why we need MLOps and business impact?
- Machine learning industrialization challenges
- How does it relate to DevOps, AIOps, ModelOps, GitOps?

**Introduction to ML and MLOps stages**:

- What are the various stages in ML lifecycle
- Detailed MLOps Principles and stages o Versioning o Testing o Automation (CI/CD) o Reproducibility o Deployment o Monitoring
- MLOps Architectures:

- Architectures \w Open Source tools o Architectures \w cloud Native tools - AWS, GCP and Azure o Comparison among cloud native tools o Cost-benefit approach of each architecture and MLOps maturity
- List of tools involved in each stages (MLOps tool ecosystem)
- MLOps Maturity Model
- Team ownership types in various stages of MLOps

**SESSION - 2 (Beginner) - DevOps - 8 hrs**

**Introduction to Git**:

- Overview of Git
- Understanding branching strategies and REPO
- Standard GIT branching strategies(development,feature, bug, release, UAT)
- Practicing important Git commands
- Github Action overview and working

**Introduction to CI/CD**:

- Introduction to CI and CD
- CI/CD challenges in Machine Learning
- Steps involved in the CI/CD implementation in ML lifecycle and workflow
- Glimpse of popular Tools used in the DevOps ecosystem on 1 cloud - e.g. AzureDevOps or

CloudBuild or cloudformation

**Cloud Native CI/CD Tools 101**:

- AzureDevOps OR Cloud Build or cloud formation

**Hands-on**:
Workshop on AzureDevOps with an industry usecase showing how CI and CD on an ML Model
OR
Workshop on CloudBuild with an industry usecase showing how CI and CD on an ML Model
AWS

**SESSION - 3 (Intermediate) - Kubernetes - 8 hrs**

**Kubernetes Overview**
- Kubernetes overview
- Kubernetes Architecture o Nodes o Control Plane o API Server
- Kubernetes Resources o Pod o Deployment o Replica o Service o Volumes (PVC)
- Kubernetes Deployment Strategy o Monitoring o Liveness and Readiness Probes o Labels and Selectors

**Hands-on**:
**1. Kubernetes kubectl command practice**
**2. Practice core concepts like Deploying Pod, deployment, ReplicaSet, Namespaces,**

**imperative commands**
**3. Use YAML construct for declarative commands**
**4. Create and Deploy ML pipeline on Kubernetes**

**SESSION - 4 (Advance) - 12hrs**

**Introduction to Model Management**
- What is a Model Management
- What are the various activities in Model Management
- Highlevel overview of below Model Management tools o MLFlow o DVC

**Cloud ML Services 101**
- What is VertexAI / AzureML Services/Sagemaker
- Various components of VertexAI/AzureML Services
- Benefits of using VertexAI/AzureML Services

**Hands-on**:

- Building ML pipeline in AzureML services/ VertexAI
- Deploying model into AKS/GKE

**SESSION - 5 (Advance) - 7 hrs**

**Introduction to Model Monitoring**
- Why monitoring is important?
- What are the various types of monitoring related to model
- Architecture of monitoring ecosystem in Azure/GCP
- Various monitoring tools on cloud

**Hands-on**:

- Building a drift monitoring system on AzureMl Services or VertexAI Sagemaker

**SESSION - 6 (Advance) - 1 hr**
**Introduction to automl tools**
- H20
- Dataiku
- Domino
- Datarobot

**Job Type**: Freelance
Contract length: 10 days

**Salary**: ₹400,000.00 - ₹480,000.00 per month

**Benefits**:

- Work from home

Schedule:

- Day shift

Application Question(s):

- Are you willing to work from 9 AM to 1 PM on weekdays?

**Education**:

- Bachelor's (preferred)

**Experience**:

- MLOps: 4 years (required)
- total work: 5 years (required)

**Language**:

- English (required)