Course / Short interactive
Machine Learning Operations
Embark on a comprehensive journey into Artificial Intelligence (AI) with this course, designed to equip learners with the fundamental principles and skills necessary for a successful career in the field of AI.
Course Name
Machine Learning Operations Short Interactive Program
Course Duration
6 hours
Course Engagement
Live Classes
Rewards
Certification
When do you want to start?
Days left to apply
No. of seats left
25
Started in IT thanks to Blockverse
MLOps Short Course training from BlockVerse Academy
Are you fascinated by the intersection of machine learning and operational efficiency? The ML Operations Short Interactive Program from BLockVerse Academy is tailored to provide a concise yet comprehensive understanding of ML Operations. Engage in interactive sessions that delve into the operational intricacies of deploying and managing machine learning models effectively.
Eager to embark on this focused learning journey? Enroll with us now. If you’re looking for more details about our ML Operations Short Interactive Program, our Academy, and the potential opportunities it unlocks, keep reading; we’ve got you covered.
You will learn this
- Gain hands-on experience in managing the entire ML development lifecycle.
- Master TFX for data processing, validation, and schema management.
- Design end-to-end ML pipelines incorporating critical components.
- Explore various deployment options and strategies for high-performance modelling.
- Develop skills in building RESTful APIs for model deployment using Flask.
- Understand the fundamentals of containers, Docker, and Kubernetes.
Module 1 : Introduction to the ML Lifecycle and TFX
- Understanding the ML development lifecycle
- Overview of TensorFlow Extended (TFX)
- The role of TFX in MLOps
Module 2 : Data Processing and Transformation using TFX
- Data ingestion and preprocessing with TFX
- Data validation and schema management
- Feature engineering and transformation pipelines
Module 3 : Authoring a Pipeline using TFX
- Designing end-to-end ML pipelines
- Incorporating components like ExampleGen, StatisticsGen, etc.
- Workflow orchestration and execution
Module 4 : Deployment Topologies and High Performance modelling
- Exploring deployment options for ML models
- Strategies for deployment, deployment modes
- Addressing scalability and resource considerations for training
Module 5 : Model Deployment using Flask
- Building RESTful APIs for model deployment with Flask
- Handling model requests and responses
- Best practices for production-ready deployment
Module 6 : Deployment using containers.
- Introduction to Docker
- Introduction to Kubernetes and architecture
Key Techniques for a Machine Learning Operations (MLOps) Professional:
TensorFlow Extended (TFX)
Delve into the world of TFX and understand its pivotal role in MLOps. Learn how to leverage TFX to enhance the efficiency of your machine learning pipelines.
Data Processing and Transformation with TFX
Master the art of data processing and transformation using TFX. From data ingestion to preprocessing, validation, schema management, and feature engineering, become adept in creating robust pipelines.
Workflow Orchestration and Execution
Learn the essential techniques for designing end-to-end ML pipelines. Understand how to incorporate critical components like ExampleGen, StatisticsGen, and others. Explore the intricacies of workflow orchestration and execution.
Deployment Topologies and High-Performance Modeling
Dive into various deployment options for ML models and understand effective deployment strategies. Address scalability considerations and gain proficiency in deploying models using Flask. Learn the art of building RESTful APIs for model deployment and adopt best practices for production-ready deployment.
Containers, Docker, and Kubernetes
Grasp the fundamentals of containers, Docker, and Kubernetes. Understand their architecture and learn how containers play a pivotal role in deploying machine learning models efficiently.
Why we should go with this
Started in IT thanks to Blockverse
Machine Learning Operations - A Strategic Skill Set
Embarking on the MLOps journey is a strategic decision, and understanding the advantages of acquiring this skill set is essential. Here’s why:
-
In-depth knowledge of the ML lifecycle enhances your capabilities in managing end-to-end machine learning projects.
-
Acquiring skills in workflow orchestration and deployment methodologies prepares you for real-world ML challenges.
-
The deployment of modelsequips you with production-ready deployment expertise.
Whether you’re aiming to advance your career or enhance your understanding of MLOps, this course opens doors to a realm where machine learning meets operational efficiency. Join us for a transformative learning experience.
