[Webinar] Challenges of ML at Scale + Hierarchical Forecasting with scikit-hts

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[Webinar] Challenges of ML at Scale + Hierarchical Forecasting with scikit-hts

September 20 @ 12:00 pm - 1:00 pm EDT

RSVP Webinar: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154

Zoom link: https://us02web.zoom.us/j/82308186562

Talk #1: Top challenges and considerations for adopting Machine Learning at Scale by Shelbee Eigenbrode, Principal AI and Machine Learning Specialist Solutions Architect, AWS

In this session, we will talk about the top challenges organizations run into when adopting machine learning at scale.
As organizations complete their first few successful prototypes and begin to realize the benefits of using machine learning to drive business outcomes, the next question is usually “How can we adopt and scale our machine learning strategy to drive more business outcomes?”. To address this question, we’ll address the top challenges and considerations when approaching these challenges. We’ll discuss choosing the right projects and MLOps among other topics in this session.

Talk #2: Hierarchical Forecasting with scikit-hts and Amazon SageMaker by Mani Khanuja and Farooq Sabir, Senior Solution Architects for AI/ML @ AWS

Time series forecasting is a very common and well known problem in machine learning and statistics. Most of the times, the time series data follows a hierarchical aggregation structure. For e.g. in retail, weekly sales for a SKU at a store can roll up to different geographical hierarchies at city, state or country level. In these cases we need to ensure, that the sales estimates are in agreement, when rolled up to a higher level.

In such scenarios, Hierarchical Time Series Forecasting, which is the process of generating coherent forecasts (or reconciling incoherent forecasts), allowing individual time series to be forecast individually, but preserving the relationships within the hierarchy, is used.
Many customers are either using hierarchical forecasting methods or have an upcoming use case that requires hierarchical forecasting to achieve better results. In this session, we will demonstrate, how to set up data for hierarchical forecasting, and use Prophet model to carry out forecasting using SageMaker framework and features.

Mani Khanuja is an Artificial Intelligence and Machine Learning Specialist SA at Amazon Web Services (AWS). She helps customers using machine learning to solve their business challenges using the AWS. She spends most of her time diving deep and teaching customers on AI/ML projects related to computer vision, natural language processing, forecasting, ML at the edge, and more.

Farooq Sabir is an Artificial Intelligence and Machine Learning Specialist Solutions Architect at Amazon Web Services (AWS). He works with customers to solve problems in artificial intelligence, computer vision, machine learning and optimization domains. Prior to joining AWS, he has worked as a lead data scientist at AT&T. He holds a PhD in Electrical Engineering from The University of Texas at Austin

Related Links
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