45 daysLive MLOps Training with Projects

Take ML models to production — the MLOps way.

A complete, end-to-end MLOps program: Linux, Git, DVC, Docker, Kubernetes, CI/CD, monitoring, MLflow, TFX, Kubeflow — and MLOps on AWS and Azure. Real-world projects and live classes on weekends.

Reserve your seat → Book a free demo

Live weekend classes · hands-on labs and assignments in every module

45 daysProgram duration

14Modules

25+Hands-on labs

WeekendsLive classes

4Real-world projects

AWS + AzureCloud MLOps

The MLOps lifecycle you'll mastertap a stage

Each stage opens the matching modules in the curriculum below.

Tools and platforms you'll work with

LinuxGitGitHubGitLab CIDVCCMLDockerKubernetesPrometheusGrafanaJenkinsMLflowTFXKubeflowFlaskAmazon SageMakerAWSAzure MLAzure DevOps

Program highlights

Built the way MLOps is done at work.

📺Live weekend classesLearn live with your instructor — ask questions as you go.
🧪25+ hands-on labsDocker, Kubernetes, Prometheus, Grafana, Jenkins and more.
📝Assignments with solutionsEvery module ends with an assignment — and a worked solution.
🏗️4 real-world projectsFrom a Flask deployment to an image classifier in TensorFlow.
☁️AWS & Azure MLOpsSageMaker pipelines and Azure ML + Azure DevOps, end to end.
🧭From zero to productionStarts with Linux and Git — no DevOps background needed.

Your learning path

From the command line to cloud MLOps.

🐧

Linux & Git01

🗂️

DVC & CI for ML02

🐳

Docker & Kubernetes03

📈

Monitoring & Jenkins04

🧪

MLflow, TFX, Kubeflow05

☁️

AWS & Azure MLOps06

Complete curriculum

14 modules. 200+ topics.

What is MLOps & MLOps motivation
Solutions and future trends
MLOps components
Different roles involved in MLOps (ML engineering + operations)
Machine learning life cycle
MLOps vs DevOps
Major phases — what it takes to master MLOps
Different tools for MLOps
MLOps maturity model levels
MLOps — stages of CI/CD

Why Linux? Linux types
How to access a Linux environment on different systems
Free-tier Amazon EC2 Ubuntu instance
SSH, SSH tools & PuTTY
FileZilla & WinSCP
Introduction to shell, Bash shell & basic Linux commands
Help for the command line
Linux core concepts & kernel types
Linux file system, boot sequence, run levels, file types & filesystem hierarchy
Package management — introduction and configuration
Package managers by Linux type
RPM and YUM
DPKG and APT
File compression and archival
Searching for files and patterns using grep / wildcards
vi and nano editors
Security and file permissions — the security incident (story)
Linux accounts, user management, access control files, account management
File permissions and ownership, cron jobs
Service management with systemd — working overtime (story)
Creating a systemd service, systemd tools
Lab — systemd services
Assignment + solution

What? Why? When? Types? Vendors? Pricing? Industry-wise uses of Git
Creating GitHub / GitLab / Bitbucket accounts
Local GitHub UI installation, setup with VS Code and PyCharm
Local and remote repositories — installation and configuration
Git repository initialisation, git log
Git branches — what branching is and why we need it
Main branch and user-defined branches
Checkout and pushing to a branch, merging branches
Project control and management
Remote repositories — initialisation
Pushing code to remote repositories
Cloning remote repositories to local
Pull requests, fetch and pull
Handling merge conflicts, forking a repository
Rebasing, resetting, reverting and stashing
Assignment + solution

What is DVC, DVC uses, installation on macOS, Windows & Linux
Data versioning, model versioning
Data access, model access & data pipelines — metrics, parameters, plots
Run, queue, compare, persist and share experiments
Clean up, versioning data and models, sharing data and model files
Data registries, shared development server & project structure
Set up a Google Drive remote, large dataset optimisation
External dependencies, managing external data
Automate pipelines with DVC — pipelines & experiment automation
Experiment management — reproducible pipelines
Common issues with ML experiments
Tracking metrics and plots, comparing experiment results — build, test & deploy
Introduction to CI/CD in machine learning & building a CI/CD pipeline
Install GitLab Runner and trigger a CI/CD pipeline
Build an ML pipeline, build and trigger a CI/CD pipeline
Making continuous integration work with ML — DVC integration with a project
Build a model prototype in a Jupyter notebook
Version your code with Git
Create and automate pipelines and data versioning with DVC
Create a CI pipeline to build, test and experiment
Experimenting with DVC and CML & deploying your model
Assignment + solution

What is DevOps, why DevOps
Dev-Test-Deploy, DevOps principles, DevOps toolchain
Overview of DevOps tools
Agile and DevOps, categories of DevOps tools
Container concepts — containers vs virtual machines
Installing Docker on CentOS, Debian and Windows
Managing containers with Docker commands
Building your own Docker images & Docker Compose
Docker registry — Docker Hub, networking inside a Docker container
Lab — run a Python web app in a Docker container
Lab — create a Docker image from a Git repo
Lab — deploy a Flask app using docker-compose
Lab — complex deployment using docker-compose
Lab — create your own Docker registry
Assignment + solution

Introduction to Kubernetes architecture and cluster installation
Raft consensus algorithm and networking in Kubernetes
Installing Minikube, Kubernetes objects — Pod, Deployment
Services — service discovery, service object, headless services, service types
Role-based access control
Volumes — persistent volumes, persistent volume claims, storage classes
ConfigMaps and Secrets
Ingress — virtual host, fanout, ingress configuration
Lab — install Minikube on EC2
Lab — enable and access the dashboard add-on
Lab — deploy a Flask web app on Minikube
Lab — deploy an Nginx app on Minikube
Lab — deploy an application with host-type volumes
Lab — create an Elastic File System on AWS
Lab — deploy Nginx using a PersistentVolume from AWS EFS
Lab — create an AWS storage class backed by EBS
Lab — deploy a Flask app as a DaemonSet
Lab — run a Kuard pod to view a secret
Lab — access a Flask app without a service
Lab — access a Flask app through a service
Lab — deploy and access a headless service
Assignment + solution

Introduction to Prometheus
Prometheus installation
Introduction to Grafana
Grafana installation
Integrating Prometheus and Grafana
Adding a customised dashboard in Grafana
Introduction to Node Exporter
Integrating Node Exporter for monitoring
Lab — scrape metrics into Grafana
Lab — view Node Exporter metrics in Grafana
Lab — view Docker metrics in Grafana
Lab — import an AWS EC2 dashboard in Grafana
Assignment + solution

Introduction to Jenkins
Continuous integration & CI with Jenkins
Jenkins architecture
Installing Jenkins on EC2
User management
Setting up Jenkins master & agents
Setting up a CI/CD pipeline for a sample project
Lab — set up role-based access
Lab — master/agent setup
Lab — configure SCM in Jenkins
Assignment + solution

What is MLflow & installation
MLflow Tracking — where runs are recorded, how runs and artifacts are recorded
Scenario 1: MLflow on localhost
Scenario 2: MLflow on localhost with SQLite
Scenario 3: MLflow on localhost with a tracking server
Scenario 4: MLflow with a remote tracking server, backend and artifact stores
Logging data to runs, logging functions, launching multiple runs in one program, performance tracking with metrics
Visualising metrics, automatic logging
Integrations — scikit-learn, TensorFlow & Keras, Gluon, XGBoost, PyTorch
MLflow tracker — organising runs in experiments, the Tracking Service API, Tracking UI
Querying runs programmatically — tracking servers, storage, networking
Logging to a tracking server, MLflow Projects — specifying, running, iterating quickly, multi-step workflows
MLflow Models — storage format, model signature and input example
Model API, built-in model flavours, model customisation, built-in deployment tools, deploying to custom targets
Model Registry — workflows, UI workflow, registering and using models, API workflow
Adding an MLflow model to the registry, fetching a model from the registry
Serving an MLflow model from the Model Registry
Adding or updating model descriptions, renaming a model
Transitioning a model's stage, listing and searching models
Archiving and deleting MLflow models
Assignment + solution

Introduction to TFX
Data ingestion using TFX & data validation using TFDV
Data preprocessing using TFT
Model training, model analysis & model evaluation using TFX
Model deployment using TF Serving
Assignment + solution

What is Kubeflow?
Core Kubeflow components
Setting up Kubeflow on Kubernetes
Developing basic ML models in Kubeflow Notebooks
Training and deploying models in Kubeflow
Using Kubeflow Pipelines
Using KFServing to deploy models
Managing logs with the Kubeflow Metadata component
Katib hyperparameter tuning
Kubeflow Pipelines to KFServing
Assignment + solution

GitLab foundation
GitLab triggers
AWS S3 storage
GitLab CI/CD pipelines
Pipeline definitions
MongoDB Cloud Atlas
Heroku
Log data
Coral for monitoring
Assignment + solution

Amazon SageMaker
Amazon S3
AWS CodeBuild
AWS CodeCommit
SageMaker training jobs
SageMaker endpoints
Amazon API Gateway
SageMaker Model Monitor
CloudWatch Synthetics
CloudWatch alarms
Assignment + solution

Create an Azure Machine Learning workspace
Set up a new project in Azure DevOps
Import an existing YAML pipeline into Azure DevOps
Declare variables for the CI/CD pipeline
Create training compute
Train an ML model
Register the model
Deploy the model to AKS
Assignment + solution

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Projects

Build, deploy and ship real projects.

PROJECT 01📧Churn Email Inbox with PythonAn end-to-end project you'll version, automate and deploy.

PROJECT 02🚲Forecast Bike RentalsA forecasting model taken through the full MLOps pipeline.

PROJECT 03🚀Deploy ML Models to Production with FlaskServe a trained model as a production web service.

PROJECT 04🖼️Neural Network for Image ClassificationBuild and ship an image classifier with TensorFlow.

Meet your trainer

Abhishek Kumar Singh

Data Scientist · Consultant · Corporate Trainer

8+ yrsData science, AI & big data

45+Corporate trainings delivered

Connect on LinkedIn

Over the last 8+ years, Abhishek has trained thousands of professionals and students across data science, MLOps and generative AI — including 45+ corporate programs for leading organizations.

Technology stack

PythonMachine LearningDeep LearningSparkKafkaAirflowAWSAzureGCPMLOpsGenerative AI

Organizations trained

DeloitteGSKShellCapgeminiMetLifeTruistJP MorganWells FargoElevance HealthFDIC

Reviews

What learners say about our cloud & ML training.

Abhishek is a teacher at heart! He brings in strong teaching experience in data science, AWS concepts such as S3, IAM, EC2, Lambda, VPC , ECS, EKS and SAGE MAKER. He is a thorough gentleman with patience, always handling Q&A with grace and treating students with unwavering respect.

Balu GaddamDirector · Truist Bank

I highly recommend Abhishek for his exceptional training on AI and ML basics. Abhishek has a talent for breaking down complex concepts into easy-to-understand segments, making learning engaging and accessible for everyone. His clear and structured approach, combined with practical examples, ensures that learners grasp the fundamentals effectively.

SriLaxmiValli GantiSenior AI Engineer · ElevanceHealth

Overall course is very comprehensive and well structured. Ideal for beginners and professionals aiming to deepen their cloud expertise. Highly recommended!

Ketan RanglaniData Engineer · Capgemini

For Aws, I think it's the best course available. The course curriculum is also designed to prepare you interview ready.

Aniket SurjeyBig Data Engineer · Capgemini

Thank you, Sir, for the insightful and well-structured AWS course. Your clear explanations and practical examples made complex concepts easy to understand and apply.