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 temperature simulator module to an IoT Edge device using a container. Check that the module was successfully created and deployed and view simulated data.

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.

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.

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.

Learn to deploy machine learning models as APIs using FastAPI for efficient application integration.

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.

Learn to Deploy Machine Learning Models. Learn about Server and Server less Frameworks Both using Python

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.

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.

This course covers the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.

Google Cloud AI offers a wide range of machine learning services. AutoML features cutting-edge technology which uses your training data to find the best model for your use case. In this course, you'll learn to build a custom machine learning model.