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Your guide to artificial Intelligence and machine learning at re:Invent 2019

With less than 40 days to re:Invent 2019, the excitement is building up and we are looking forward to seeing you all soon!

Continuing our journey on artificial intelligence and machine learning, we are bringing a lot of technical content this year, with over 200 breakout sessions, deep-dive chalk talks, hands-on exercises with workshops featuring Amazon SageMaker, AWS DeepRacer, and deep learning frameworks such as TensorFlow, PyTorch, and more.

Amazon SageMaker deep dive: A modular solution for machine learning (Session AIM307) Amazon SageMaker is a fully managed service enabling all developers and data scientists with every aspect of the machine learning workflow.

Starting the enterprise machine learning journey (Session AIM205) Amazon has been investing in machine learning for more than 20 years, innovating in areas such as fulfillment and logistics, personalization and recommendations, forecasting, fraud prevention, and supply chain optimization.

In this breakout session, learn how to use language and vision AI services to extract data, insights, and trends from all of your digital content, with a focus on how to more effectively manage your documents and find what you need.

Improve machine learning model quality in response to changes in data (Session AIM213) Machine learning models are typically trained and evaluated using historical data.

In these sessions, chalk talks, and workshops, we will dive deep into the practical aspects of machine learning for specific industries including finance, healthcare, retail, media and entertainment, manufacturing, and more.

In this session, we cover AI solutions specific to the Healthcare industry, from extracting relevant medical information from patient records and clinical trial reports to automating the clinical documentation process with automatic speech recognition.

ML in retail: Solutions that add intelligence to your business (Session AIM212) Machine learning is ranked the number-one “game changer” for the retail market segment by chief experience officers (CXOs), yet it’s only number eight on top spending priorities.

Amazon Textract uses artificial intelligence to “read” such documents as a person would, to extract not only text but also tables, forms, and other structured data without configuration, training, or custom code.

Predict future business outcomes using Amazon Forecast (Session AIM312) Based on the same technology used at, Amazon Forecast uses machine learning and time-series data to build accurate business forecasts.

In this session, learn how machine learning can improve accuracy in demand forecasting, financial planning, and resource allocation while reducing your forecasting time from months to hours.

Build predictive maintenance systems with Amazon SageMaker (Chalk Talk AIM328) Across a wide spectrum of industries, customers are starting to use prediction maintenance models to proactively fix problems before they impact production.

We dive deep into the architecture for using the turbofan degradation simulation dataset to train the model to recognize potential equipment failures and share details.

Accelerate time-series forecasting with Amazon Forecast (Workshop AIM335) Based on the same technology used at, Amazon Forecast uses machine learning to combine time-series data with additional variables to build up to 50% more accurate forecasts.

In this workshop, prepare a dataset, build models based on that dataset, evaluate a model’s performance based on real observations, and learn how to evaluate the value of a forecast compared with another.

In this workshop, you build your own recommendation engine by providing training data, building a model based on the algorithm of your choice, testing the model by deploying your Amazon Personalize campaign, and integrating it into your own application.

These code-level sessions and hands-on workshops will enable the advanced developer or data scientist in you to customize, integrate, and solve many challenges with deep technical solutions.

The advanced breakout session will dive deep into training machine learning models with TensorFlow using Amazon SageMaker, including distributed training, cost-effective inference, and workflow management.

The code-level workshop will include hands-on exercises where we will train and deploy TensorFlow models, apply automatic model tuning using Amazon SageMaker, and make predictions in production.

In the breakout session, you will lern how to develop deep learning models with PyTorch using Amazon SageMaker for multiple use cases including using a BERT model and instance segmentation for fine-grain computer vision.

Under the hood of AWS DeepRacer: Advanced RL driving course (Workshop AIM428) This technical deep dive is suitable for advanced machine learning developers looking to learn more complex reinforcement learning concepts using AWS DeepRacer and Amazon SageMaker RL.

AWS data scientists help you build models that require innovations in neural network architecture, expand the algorithms, and help you customize your AWS DeepRacer model for performance.

Security for ML environments with Amazon SageMaker (Session AIM327) Amazon SageMaker is a modular, fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.

Vanguard joins us to discuss the company’s use of Amazon SageMaker and its implementation of key controls in a highly regulated environment, including fine-grained access control, end-to-end encryption in transit, and comprehensive audit trails for resource and data access.

Whether you are a data scientist, machine learning practitioner, or business professional, you’ll enjoy the Machine Learning Summit at this year’s re:Invent, which will showcase advances in machine learning as well as the emerging trends.

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