OVERVIEW:-

An MLOps course provides a comprehensive exploration of foundational concepts essential for deploying machine learning models in real-world scenarios. Participants delve into effective data management strategies tailored for MLOps environments, ensuring robustness and reliability throughout the model lifecycle. The course emphasizes integrating both traditional machine learning algorithms and advanced deep learning techniques within MLOps frameworks, fostering a holistic understanding of model deployment and optimization.
Hands-on exercises using Google Cloud Platform equip learners with practical skills in setting up scalable and efficient machine learning workflows. They gain proficiency in leveraging cloud-based services for model training, deployment automation, and monitoring, essential for maintaining peak performance in production environments. Emerging technologies like Generative AI are also explored, with practical applications using tools such as Gemma, offering insights into cutting-edge developments transforming the AI landscape.
Moreover, the course addresses MLOps technologies designed to support scalable machine learning deployments. By examining these technologies, participants gain a deeper understanding of optimizing model scalability and operational efficiency. The course concludes with a forward-looking discussion on the evolving landscape of MLOps, highlighting trends, challenges, and future directions shaping the field’s role in advancing AI applications across industries.

Exam Included:
Including Red Hat Openshift AI Exam – EX267 with 2nd Attempt 

Prerequisite:
No Prerequisite. Python basics will be added advantage.

MLOps

Module: Python for ML and Jupyter Notebook
  • Python for ML
  • Jupyter Notebook
  • Hands On: Create a Jupyter Notebook to explore basic Python
Module: Introduction to AI and ML
  • Introduction to AI and ML
  • Types of learning in ML
  • Clustering in ML
  • Basic Algorithms
  • Hands On: Implement a linear regression model
Module: Data Engineering on GCP
  • Introduction to Vertex AI
  • Using BigQuery to creating and training ML Models
  • Data Traps and how to address it
  • Introduction to GCP services for data management
  •  Hands On: Create a Dataset in BigQuery and load data form CSV file
  • Hands On: Use Vertex AI
Module: Model Development and Training
  • Introduction to TensorFlow  for building and training ML models
  • Introduction to generative models
  • Use Vertex AI to manage models, experiments, and hyperparameters.
  • Introduction to Kubeflow platform for building and deploying ML pipelines
  • Introduction to AutoML techniques and tools for automated model selection
  • Hands On: Build a neural network using TensorFlow
  • Hands On: Train a generative adversarial network (GAN) to genrate Images
  • Hands On: Experiment with different hyperparameters to evaluate model performance
Module 5: Model Deployment and Serving
  • Use Vertex AI's managed services to deploy models
  • Use Docker containers to deploy models for portability and scalability
  • Use Kubernetes to orchestrate the ML workloads
  • Hands On: Deploy a trained TensorFlow model to Vertex AI
  • Hands On: Create a Docker container for your model and deploy it to a Kubernetes cluster.
  • Hands On: Build a Kubeflow pipeline to automate the ML workflow.
Module: Red Hat OpenShift AI
  • Introduction to OpenShift AI
  • Data Science Projects
  • Jupyter Notebooks
  • OpenShift AI features
  • OpenShift AI architecture
  • Installing Red Hat OpenShift AI
  • Managing Users and Resources
  • Custom Notebook Images
  • Introduction to Machine Learning
  • Training Models
  • Enhancing Model Training with RHOAI
  • Introduction to Model Serving
  • Model Serving in Red Hat OpenShift AI
  • Custom Model Servers
  • Introduction to Data Science Pipelines
  • Creating ML Pipelines on OpenShift
  • Elyra Pipelines
  • KubeFlow Pipelines
  • Integrating other tools with OpenShift AI
  • Hands On: Improving insurance claims process
  • Hands On: Connection and Setup
  • Hands On: Working with an LLM
  • Hands On: Image processing
  • Hands On: Deploy a TensorFlow model to OpenShift
  • Hands On: Create an ML pipeline on OpenShift using Kubeflow
Module 9: Red Hat OpenShift AI Certification
  • EX267 Exam Preparation and Exam267 is included in this course
Note: This course in alligned with Developing and Deploying AI/ML Applications on Red Hat OpenShift AI
Module: AI ML with AWS Services
  • Introduction to Amazon SageMaker
  • Introduction to Amazon Bedrock
  • Introduction to Amazon CodeWhisperer
  • Introduction to Amazon Rekognition
  • compare Amazon SageMaker with Vertex AI
  • Hands On: Compare cloud platforms and their services regarding AI and ML
  • Hands On: Use AWS AI ML services
Module: Model Monitoring and Management
  • Explore techniques for managing ML infrastructure efficiently
  • Bias, fairness, and explainability in AI and ML
  • Introduction to Model Drift and how to address it
  • Best practices for model governance and compliance.
  • Hands On: Implement model drift detection using a drift detection technique
  • Hands On: Retain a model on new data to improve its performance.
  • Hands On: Use autoscaling and resource management to Optimise ML infrastructure
Module: Mini and Major Projects
  • Mini Project: AI-ML with Google
  • Mini Project: AI-ML with AWS
  • Mini Project: AI-ML with Red Hat OpenShift AI
  • Major Project: A major project on all the technologies from above mentioned course

Training Partners

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