Beginner to Advanced MLOps on GCP-CI/CD, Kubernetes Jenkins
What you'll learn
- Build and manage robust continuous integration and deployment pipelines using tools like GitHub Action and Jenkins tailored for machine learning s, GitLab CI/CD
- Utilize containerization and orchestration tools such as Docker, Kubeflow, and Minikube to create scalable, production-ready ML systems on GCP.
- Efficiently manage and secure ML data with PostgreSQL while implementing real-time monitoring and visualization dashboards using Grafana.
- Apply best practices in scaling, resource management, and security compliance to ensure efficient and secure ML operations in cloud environments.
Requirements
- Programming Proficiency: Basic to intermediate experience with programming, particularly in Python, which is widely used in machine learning and scripting for automation.
- A basic understanding of machine learning principles
Description
This Beginner to Advanced MLOps Course covers a wide range of technologies and tools essential for building, deploying, and automating ML models in production.
Technologies & Tools Used Throughout the Course
Experiment Tracking & Model Management: MLFlow, Comet-ML, TensorBoard
Data & Code Versioning: DVC, Git, GitHub, GitLab
CI/CD Pipelines & Automation: Jenkins, ArgoCD, GitHub Actions, GitLab CI/CD, CircleCI
Cloud & Infrastructure: GCP (Google Cloud Platform), Minikube, Google Cloud Run, Kubernetes
Deployment & Containerization: Docker, Kubernetes, FastAPI, Flask
Data Engineering & Feature Storage: PostgreSQL, Redis, Astro Airflow, PSYCOPG2
ML Monitoring & Drift Detection: Prometheus, Grafana, Alibi-Detect
API & Web App Development: FastAPI, Flask, ChatGPT, Postman, SwaggerUI
How These Tools & Techniques Help
Experiment Tracking & Model Management
Helps in logging, comparing, and tracking different ML model experiments.
MLFlow & Comet-ML provide centralized tracking of hyperparameters and performance metrics.
Data & Code Versioning
Ensures reproducibility by tracking data changes over time.
DVC manages large datasets, and GitHub/GitLab maintains version control for code and pipelines.
CI/CD Pipelines & Automation
Automates ML workflows from model training to deployment.
Jenkins, GitHub Actions, GitLab CI/CD, and ArgoCD handle continuous integration & deployment.
Cloud & Infrastructure
GCP provides scalable infrastructure for data storage, model training, and deployment.
Minikube enables Kubernetes testing on local machines before deploying to cloud environments.
Deployment & Containerization
Docker containerizes applications, making them portable and scalable.
Kubernetes manages ML deployments for high availability and scalability.
Data Engineering & Feature Storage
PostgreSQL & Redis store structured and real-time ML features.
Airflow automates ETL pipelines for seamless data processing.
ML Monitoring & Drift Detection
Prometheus & Grafana visualize ML model performance in real-time.
Alibi-Detect helps in identifying data drift and model degradation.
API & Web App Development
FastAPI & Flask create APIs for real-time model inference.
ChatGPT integration enhances chatbot-based ML applications.
SwaggerUI & Postman assist in API documentation & testing.
This course ensures a complete hands-on approach to MLOps, covering everything from data ingestion, model training, versioning, deployment, monitoring, and CI/CD automation to make ML projects production-ready and scalable.
Who this course is for:
- Machine Learning Engineers & Data Scientists: Those who want to bridge the gap between model development and scalable deployment.
- DevOps & MLOps Practitioners: Individuals aiming to integrate CI/CD pipelines and container orchestration into ML workflows.
- Cloud & Infrastructure Specialists: Professionals seeking to deepen their expertise in GCP and related cloud-native tools.
- Technical Leaders & Architects: Decision-makers responsible for designing and maintaining robust, scalable ML systems in production.
Instructors
- 4.6 Instructor Rating
- 41,364 Reviews
- 213,413 Students
- 13 Courses
Krish AI Technologies is at the forefront of education in the fields of Data Science, Machine Learning, Generative AI, Deep Learning, and related technologies. Founded by industry veteran Krish Naik, who has over 13 years of experience in the data analytics industry and more than 7 years of teaching expertise, our mission is to equip learners with the skills and knowledge required to excel in the rapidly evolving tech landscape.
Our Expertise: At Krish AI Technologies, we specialize in a comprehensive range of subjects within the realm of artificial intelligence and data science, including:
Data Science: From foundational concepts to advanced techniques, we cover all aspects of data analysis, statistical modeling, and data visualization.
Machine Learning: Our curriculum spans the full spectrum of machine learning algorithms, including supervised and unsupervised learning, clustering techniques, and advanced predictive modeling.
Generative AI: We provide in-depth training on the latest generative AI models and techniques, helping students understand and implement cutting-edge technologies.
Deep Learning: Our courses delve into the mathematical intuition and practical applications of deep learning, covering neural networks, CNNs, RNNs, and more.
Natural Language Processing (NLP): We offer comprehensive training in NLP, including text preprocessing, sentiment analysis, language modeling, and various NLP projects.
- 4.4 Instructor Rating
- 182 Reviews
- 3,919 Students
- 1 Course
I’m Sudhanshu Gusain, a Data Science, Machine Learning, and AI instructor with years of hands-on experience in both teaching and real-world applications. My goal is to simplify complex concepts and help learners build strong foundations in data-driven technologies.
I have worked extensively on practical projects across various industries, applying AI and ML techniques to solve real-world problems.
Alongside my Udemy courses, I run a YouTube channel, DATA GURU, where I share insightful tutorials, industry trends, and practical case studies to help aspiring data scientists and AI enthusiasts upskill effectively.