AI & Robotic
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Demystifying the AWS Certified Machine Learning Specialty Exam
The AWS Machine Learning Specialty Exam covers four distinct domains. This course will review the exam, the concepts from these four domains, and techniques for how you can best prepare for it.
Deploy a pre-built module to the Edge device
Deploy a pre-built temperature simulator module to an IoT Edge device using a container. Check that the module was successfully created and deployed and view simulated data.
Deploy an AI-powered Discord Bot with a Voice
Learn how to deploy a voice-enabled AI-powered bot for Discord. Integrate advanced AI models to create engaging and interactive Discord communities.
Deploy Azure AI services in containers
Learn about Container support in Azure AI services allowing the use of APIs available in Azure and enable flexibility in where to deploy and host the services with Docker containers.
Deploy model to NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is a multi-framework, open-source software that is optimized for inference. It supports popular machine learning frameworks like TensorFlow, Open Neural Network Exchange (ONNX) Runtime, PyTorch, NVIDIA TensorRT, and more. It can be used for your CPU or GPU workloads. In this module, you deploy your production model to NVIDIA Triton server to perform inference on a cloud-hosted virtual machine.
Deploy Neural Network Regularizers to Prevent Overfitting
Learn how to deploy neural network regularizers like dropout and L2 regularization to prevent overfitting in your models. Understand how regularization techniques improve model generalization, ensuring better performance on unseen data.
Deploy Trained Models
Technology provides a competitive edge to organizations which makes the need to understand machine learning even more important. This course will help you better understand how to deploy trained machine learning models to a production environment.
Deploying a Machine Learning Model with FastAPI
Learn to deploy machine learning models as APIs using FastAPI for efficient application integration.
Deploying and Managing Models in Microsoft Azure
In this course, you'll learn about how data science practitioners can utilize tools for managing the models they create. You'll also see those tools showcased in Microsoft Azure.
Deploying Machine Learning Models – A Complete Guide
Learn to Deploy Machine Learning Models. Learn about Server and Server less Frameworks Both using Python
Deploying Machine Learning Models to Production: Challenges & Solutions
In this presentation, you will look at the top challenges you face deploying machine learning models to production and how to tackle those challenges using MLOps.
Deployment Isn’t the Final Step: Monitoring Machine Learning Models in Production Environments
In this session, we will talk about the data science project cycle which holds five main stages - defining your project objectives, collecting and cleaning your data, training and testing a predictive model, deploying it and monitoring.