Accelerating Deployment of scalable AI Solution Series

Data Analytics workflows have traditionally been slow and cumbersome when relying on CPU to compute for data preparation, training and deployment. Accelerated data science with the GPU and latest Spark 3.x and RAPIDS software stack can dramatically boost the performance of end-to-end analytics workflows, speeding up value generation while reducing cost.

In this one-day course, you will learn:

•        How Spark 3.x and RAPIDS works?

•        What is GPU accelerator in Spark 3.x?

•        Hands-on experience with Spark 3.x with RAPIDS to conduct Big Data processing and Machine Learning with GPU acceleration

•        Hands-on experience in building AI pipelines using Elyra on an actual use case.

The challenge in deploying large-scale AI applications is the method of managing the entire AI lifecycle from start to production. This course covers how to use Spark 3.x and MLflow to address these challenges.

In this one-day course, you will learn:

•    Essential knowledge in DevOps and MLOps

•   Container technology & Kubernetes (K8s)

•   How to build  microservices for scalable AI applications following K8s concept

•   How to do the blue/green deployment to ensure service continuity

•   Hands-on experience in deploying scalable AI applications in Kubernetes

The problem that lies in the development of large-scale AI applications is at the data processing stage which involves scaling Big data. This course covers how to use Spark 3.x and MLflow to address these challenges.

In this three-day course, you will learn:

•        How Spark 3.x and RAPIDS works?

•        What is GPU accelerator in Spark 3.x?

•        Hands-on experience on in-depth usage of Spark in cluster mode

•        Hands-on experience on streaming processing, Data Meta Info Management, accelerated hardware including GPU to expedite both data processing and machine learning.

•        Hands-on experience on an actual use case to practise using MLflow to manage the end-to-end AI lifecycle.