Selamat Datang di Konferensi Online AWS Innovate –
Edisi AI dan Machine Learning

Selamat datang di Konferensi Online AWS Innovate – Edisi AI dan Machine Learning, yang dirancang untuk menginspirasi dan membantu Anda untuk mempercepat inovasi, meningkatkan skalabilitas dengan mudah, dan membuka peluang baru. Dapatkan informasi terbaru tentang AI dan Machine Learning dari Glenn Gore, Chief Architect, AWS; Oliver Klein, Head of Emerging Technologies, AWS; dan Dean Samuels, Lead Architect, AWS dalam keynote mereka.

Pelajari lebih dalam dengan 20+ sesi breakout dalam enam track yang disampaikan oleh para ahli dari AWS; jelajahi konsep penting, kasus penggunaan, praktik terbaik, dan demo untuk mempelajari bagaimana organisasi lain menggunakan AI dan Machine Learning, serta melangkah dengan kemampuan untuk menerapkan proyek ini untuk organisasi Anda.

Pelajari bagaimana pelanggan AWS menggunakan machine learning untuk meningkatkan kualitas perawatan kesehatan, memerangi perdagangan manusia, menyediakan layanan pelanggan yang lebih baik, serta melindungi Anda dari penipuan. Dengan rangkaian layanan machine learning dan AI yang lebih luas dan lebih dalam, pelanggan AWS membuat wawasan baru, memungkinkan efisiensi baru, dan membuat prediksi yang lebih akurat. Itulah mengapa lebih dari 10,000 pelanggan telah memilih menggunakan AWS untuk machine learning.

+10.000

pelanggan memilih menggunakan AWS untuk machine learning

89%

proyek deep learning di cloud berjalan di AWS

85%

proyek TensorFlow dalam cloud terjadi di AWS

83%

proyek PyTorch dalam cloud terjadi di AWS

Deskripsi Sesi

  • Sesi Bahasa Indonesia
  • Sesi Inggris
  • Sesi Bahasa Indonesia
  • Belajar Machine Learning dan Meningkatkan Produktivitas dengan Machine Learning IDE (Tingkat 100)

    Amazon SageMaker merupakan layanan modular yang dikelola sepenuhnya, yang memungkinkan pengembang dan data scientist build dan meningkatkan skala solusi machine learning. Dengan Amazon SageMaker, Anda dapat secara langsung deploy ke dalam lingkungan host yang siap untuk produksi. Dalam sesi ini, pelajari bagaimana Amazon SageMaker Studio sebuah lingkungan pengembangan terintegrasi (IDE) untuk machine learning memungkinkan Anda build, train, melakukan debug, deploy, dan memantau model machine learning dengan mudah. Amazon SageMaker Studio menyediakan semua alat yang Anda butuhkan untuk mempercepat membawa model Anda dari eksperimentasi ke produksi, dan meningkatkan produktivitas Anda. Dalam sebuah antarmuka visual terpadu, pelanggan dapat menulis dan menjalankan kode di notebook Jupyter dan memantau kinerja prediksi mereka, serta melacak dan melakukan debug pada eksperimen machine learning.

    Pembicara: Donnie Prakoso, Senior Developer Advocate, AWS

    Mempercepat dan menyederhanakan forecasting berbasis time-series (Tingkat 200)

    Deploying model machine learning kustom untuk mengatasi tantangan bisnis yang kompleks sulit dilakukan, tetapi tidak perlu demikian. Berdasarkan teknologi machine learning yang disempurnakan dari bertahun-tahun menggunakan Amazon.com, Amazon Forecast dan Amazon Personalize memungkinkan pengembang yang tidak memiliki pengalaman machine learning sebelumnya dengan mudah build kemampuan forecasting yang akurat dan personalisasi canggih ke dalam aplikasi mereka. Menggunakan AutoML, proses baru yang mengautomasi tugas machine learning yang kompleks, layanan ini menjalankan dan mempercepat pekerjaan yang sulit, yang dibutuhkan untuk build, train, dan deploy model machine learning yang disesuaikan untuk data Anda. Dalam sesi ini, kami akan menunjukkan kepada Anda cara menggunakan Amazon Personalize dan Amazon Forecast untuk membuat rekomendasi inidividu untuk pelanggan dan memberikan forecasting yang sangat akurat. Keduanya berjalan pada infrastruktur yang dikelola sepenuhnya dan memberikan sarana yang mudah digunakan, yang menghadirkan model berkualitas tinggi, bahkan jika Anda tidak memiliki banyak pengalaman dengan machine learning.

    Pembicara: Rudi Suryadi, Senior Solutions Architect, AWS

    Membangun model Machine Learning berkualitas tinggi untuk mendeteksi serangan penipuan secara real-time (Tingkat 200)

    Setiap tahun di seluruh dunia, puluhan miliar dolar hilang akibat penipuan online. Perusahaan yang melakukan bisnis online berisiko terkena serangan aktor buruk yang seringkali menggunakan taktik yang berbeda, seperti membuat akun palsu dan melakukan pembayaran dengan kartu kredit curian. Perusahaan biasanya menggunakan aplikasi pendeteksi penipuan untuk mengidentifikasi penipu dan menghentikan mereka sebelum merugikan bisnis. Sesi ini akan membahas lebih dalam cara menerapkan solusi deteksi penipuan yang dikustomisasi untuk aktivitas online menggunakan machine learning untuk secara proaktif mengidentifikasi dan menerapkan perubahan dalam melindungi perusahaan dan pelanggan Anda.

    Pembicara: Petra Barus, Senior Developer Advocate, AWS

    Visual Analysis berbasis AI dan Machine Learning (Tingkat 200)

    Perusahaan menggunakan visi komputer untuk memahami konten dan konteks gambar dan video mereka dalam skala besar. Sesi ini memberikan ikhtisar kondisi untuk dipertimbangkan saat mengembangkan aplikasi Anda sendiri. Kami akan menjelaskan beberapa pendekatan untuk mengatasi kebutuhan visi komputer bisnis Anda dan langkah berikutnya untuk mengembangkan model untuk kasus penggunaan khusus Anda. Kita akan mengakhiri sesi ini dengan mempelajari cara menggunakan AWS DeepLens dan Amazon Rekognition untuk membangunkan aplikasi.

    Pembicara: Donnie Prakoso, Senior Developer Advocate, AWS

    Teknik deployment model ML menggunakan Amazon SageMaker Managed Deployment, Amazon Elastic Inference, Amazon Neo, dan AWS Inferentia (Tingkat 300)

    Machine learning dapat memakan banyak sumber daya dan Anda tidak akan dapat deploying model machine learning hingga model tersebut dilatih. Di AWS, kami terus bekerja untuk membuat training model lebih cepat, efisien, dan lebih murah. Meski demikian, inferensi model menjadi keunggulan dari machine learning. Di sinilah ucapan dikenali, teks diterjemahkan, objek dalam video dikenali, cacat manufaktur ditemukan, dan mobil dikendarai. Sesi ini akan menganalisis kesulitan yang kita hadapi dalam menjalankan beban kerja inferensi machine learning dan deep learning. Sesi juga akan menjelaskan bagaimana AWS mengatasi kesulitan tersebut saat Anda menambahkan kecerdasan ke aplikasi Anda dan meningkatkan skala beban kerja ini.

    Pembicara: Rudi Suryadi, Senior Solutions Architect, AWS

  • Sesi Inggris
    • Innovation at Amazon
    • Accelerate your ML journey
    • AI/ML Fundamentals
    • Build, train and deploy ML models
    • AI Services & Applications
    • AI/ML Services and Devices
    • Hands-on Labs
    • Builders' Zone Demo
    • Innovation at Amazon
    • Create tomorrow with data and machine learning (Level 100)

      Whether it is helping online shoppers automate repeat purchases, creating advanced real-time recommendations for online gamers, or accelerating new product development, businesses today are increasingly recognizing the value of collecting real time and historical data and using machine learning technology to innovate faster for customers. In this session, Glenn Gore, Worldwide Lead Solutions Architect, AWS, explores how businesses such as Amazon Retail, Amazon Alexa, and Amazon Robotics use data and machine learning to innovate for customers.

      Speakers:
      Glenn Gore, Chief Architect, AWS
      Tye Brady, Chief Technologist, Amazon Robotics
      Manoj Sindhwani, VP, Alexa Speech
      Jenny Freshwater, Director of Supply Chain
      Forecasting & Capacity Planning, Amazon
      Mike Vogelsong, Senior Machine Learning Scientist, Amazon

    • Accelerate your ML journey
    • Accelerating machine learning: Your role in the journey (Level 100)

      Artificial intelligence and machine learning (ML) hold the promise of transforming industries, increasing efficiencies, and driving innovation. Executives play important roles in accelerating the ML journey, but while many are prioritizing ML, going from idea to implementation can be daunting and can stop companies before they begin. In this session, we discuss ML implementation challenges and explain how AWS customers have been successful in introducing ML to help achieve their business goals. We share how customers are working with AWS to align teams, drive ML excitement, provide developers with the right technical education, and identify appropriate use cases to start moving from idea to production.

      Speaker: Joel Minnick, Head of AI Product Marketing, AWS

      Learn machine learning like an Amazonian (Level 100)

      Machine learning (ML) and artificial intelligence are stealing headlines and sparking the imaginations of individuals working in big organizations and the startup community. Yet, one of the biggest barriers to the adoption of ML is finding and cultivating trained talent. In this session, learn how AWS Training and Certification is building on the curriculum used to train thousands of Amazon’s developers and providing resources to help make your ML vision a reality.

      Speaker: Elly Juniper, Senior Technical Business Development Manager, AWS

      Accelerate ML projects with ML and data services from AWS Marketplace (Level 200)

      Companies spend significant time developing, searching for, and evaluating algorithms and models to solve business problems using machine learning (ML). The AWS Marketplace has hundreds of algorithms and model packages that can be deployed quickly onto Amazon SageMaker. This session provides an introduction to the AWS Marketplace and how it can help you accelerate your ML projects by using third-party models and algorithms.

      Speaker: Kanchan Waikar, Senior Partner Solutions Architect, AWS

    • AI/ML Fundamentals
    • Getting started with machine learning and improve productivity in a fully integrated development environment (Level 100)

      Amazon SageMaker is a fully managed, modular service that enables developers and data scientists to build and scale machine learning (ML) solutions. With it, you can directly deploy ML solutions into production-ready hosted environments. Come learn about Amazon SageMaker Studio, an integrated development environment (IDE) that lets you build, train, debug, deploy, and monitor ML models. This service provides the tools needed to accelerate the process of taking your models from experimentation to production and to boost productivity. In a unified visual interface, you can write and execute code in Jupyter notebooks and monitor the performance of your predictions, as well as track and debug ML experiments.

      Speaker: Kapil Pendse, Senior Solutions Architect, AWS

      Meeting security and compliance objectives when using Amazon SageMaker (Level 200)

      Amazon SageMaker is a fully managed machine learning (ML) service that data scientists and developers use to build predictive and analytical models with their data. Datasets used for research may contain sensitive information, such as medical records or intellectual property, that must be protected. In this session, we guide you through the steps needed to ensure security and compliance when using Amazon SageMaker, including using data encryption, identity and access management, logging and monitoring, compliance validation, and infrastructure security.

      Speaker: Michael Stringer, Senior Solutions Architect, AWS

      Building machine learning workflows with Kubernetes and Amazon SageMaker (Level 200)

      Until recently, data scientists had to spend significant time performing operational tasks, such as ensuring that frameworks, runtimes, and drivers for CPUs and GPUs worked well together. They also needed to design and build machine learning (ML) pipelines to orchestrate complex workflows for deploying ML models in production. In this session, we dive into Amazon SageMaker and container technologies and discuss how easy it is to integrate tasks such as model training and deployment into Kubernetes and Kubeflow-based ML pipelines. Further, we show how the new Amazon SageMaker Operators for Kubernetes makes it easier to use Kubernetes to train, tune, and deploy ML models in Amazon SageMaker.

      Speaker: Arun Balaji, Partner Solutions Architect, AISPL

      Evolution of personalization and recommendation for video workflows (Level 200)

      Personalizing the user experience is proven to increase discoverability, user engagement and satisfaction, and revenue. However, many AWS customers find personalization difficult to do correctly. Effective recommender systems require solving multiple hard problems, including constantly changing user behavior, new catalog items (cold start), and more. In this session, we cover some of the most common methods of personalization and recommendation. Additionally, the Amazon Prime Video team shares the evolution of its recommendation system and the real-world challenges that it faced when building recommendation systems at scale.

      Speaker: Liam Morrison, Principal Specialist Solutions Architect, AWS 

      Scale machine learning from zero to millions of users (Level 200)

      Data scientists and machine learning (ML) engineers use a variety of tools that make it easy to start everyday tasks. But as models become more complex and datasets become larger, training time and prediction latency become significant concerns. In this session, we show you how to scale ML workloads using AWS services, including AWS Deep Learning AMIs and containers, Amazon ECS, Amazon EKS, and AWS Fargate. We also discuss the relative advantages of these services, and we run some interesting demonstrations.

      Speaker: Praveen Jayakumar, Solutions Architect, AISPL

    • Build, train and deploy ML models
    • Automate machine learning: From debugging deep learning to detecting model drift in production (Level 300)

      Machine learning (ML) involves more than just training models; developers need to debug these deep learning models as well as monitor their performance in production so that they serve their intended business purpose. However, models can become outdated as the nature of data changes, causing model drift in production and generating irrelevant results. This type of model degradation tends to go undetected. In this session, we cover how to help radically reduce troubleshooting time in building and training high-quality ML models and how to identify and detect drift in your ML model post-deployment.

      Speaker: Aparna Elangovan, Prototype Engineer, AI/ML, AWS

      Auto-create machine learning models with full visibility using Amazon SageMaker Autopilot (Level 300)

      If you think that machine learning (ML) sounds like a lot of experimental trial-and-error work, you are absolutely right. Building ML models has traditionally required a binary choice: either having deep expertise in-house, which is rare, or using an automated approach which gives little visibility into how the model was created. In this session, find out how you can simply call an API and get the job done, and learn how Amazon SageMaker Autopilot allows you to automatically build ML models without compromises.

      Speaker: Tapan Hoskeri, Solutions Architect, AISPL

      Machine learning deployment on AWS: Best practices to decide what, where, and how (Level 300)

      Putting ML solutions in production requires knowing the what, where, and how of deploying ML models. One needs to know what the model is (resource requirements in production) and what the business context is (input workload, output consumers, batch vs. real-time inference, etc.); where to deploy (cloud or edge, based on cost-effectiveness, fulfillment of business SLAs, etc.); and how to deploy (based on ease of deployment, scaling, A/B testing, etc.). Working backwards from these customer questions, AWS offers the broadest and deepest range of ML deployment options. This session covers these options and how they address the above questions from a best-practices perspective.

      Speaker: Sujoy Roy, Senior Data Scientist, AWS

      Accelerate the building of deep learning applications (Level 300)

      The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at any scale. Whether Amazon EC2 GPU or CPU instances are used, with AWS Deep Learning AMIs, users pay only for the AWS resources needed to store and run their applications. In this session, learn how to quickly launch Amazon EC2 instances preinstalled with popular deep learning frameworks and interfaces—such as TensorFlow, PyTorch, and Apache MXNet, Chainer, Gluon, Horovod, and Keras—to train sophisticated, custom AI models; experiment with new algorithms; or learn new skills and techniques. Whether you need Amazon EC2 GPU or CPU instances, there is no additional charge for the Deep Learning AMIs – you only pay for the AWS resources needed to store and run your applications.

      Speaker: Pedro Paez, Specialist Solutions Architect, AWS

    • AI Services & Applications
    • Amazon Fraud Detector: Detect more online fraud faster (Level 200)

      Globally each year, tens of billions of dollars are lost to online fraud. Companies conducting business online are especially prone to attacks from bad actors, who often exploit tactics such as creating fake accounts and making payments with stolen credit cards. Companies typically use fraud detection applications to identify fraudsters and stop them before they cause costly business disruptions. This session details how to implement a customized fraud detection solution for online activities, using machine learning to proactively identify and implement changes in the protection of your company and customers.

      Speaker: Eric Greene, AI Specialist Solutions Architect, AWS

      Simplify and accelerate time-series forecasting and real-time personalization (Level 200)

      Deploying custom machine learning (ML) models to solve complex business challenges does not have to be hard. Based on ML technology perfected through years of use on Amazon.com, Amazon Forecast and Amazon Personalize enable developers with no prior ML experience to easily build accurate forecasting and sophisticated personalization capabilities into their applications. In this session, we show you how these two services, using a new automating process, AutoML, create individualized recommendations for customers and deliver highly accurate forecasts. Both services run on fully managed infrastructure and provide easy-to-use recipes that deliver high-quality models even if you have little ML experience.

      Speaker: Anand Iyer, Enterprise Solutions Architect, AISPL

      Amazon Kendra: Reinvent enterprise search and interact with data using AI (Level 300)

      How can you get the most accurate and specific result to a search query when the answer is hidden within various enterprise information systems? In this session, we show you how to use Amazon Kendra, an enterprise search solution that provides straightforward answers to specific search queries, such as, “How much is the cash reward on the corporate credit card?” Learn how Amazon Kendra can improve cross-team knowledge sharing, increase sales, and enhance customer support services. Also, discover how this new service makes it easier for customers to find the information they need.

      Speaker: Will Badr, Senior AI/ML Solutions Architect, AWS

      Breaking language barriers with AI (Level 300)

      Amazon brings natural language processing, speech recognition, text-to-speech capabilities, and machine translation within the reach of every developer. API-driven application services enable data scientists and developers to easily add pre-built artificial intelligence functionality into their applications and automate workflows. In this session, learn how to build the next generation of intelligent applications that hear, speak, and understand the world around us.

      Speaker: Sara van de Moosdijk, Partner Solutions Architect, AWS

      Machine learning powered analytics for contact centers (Level 300)

      Today, most contact center analytics are based on phone switch activity or CRM call notes that are recorded by the contact center agent. However, these analytics typically lack insights into the actual conversations between agents and customers. In this session, we explain how Amazon Connect analyzes the nuances of contact center conversations, including those in different languages and those that have custom vocabularies. Discover how Contact Lens for Amazon Connect enables customer service supervisors to conduct fast, full-text searches on call and chat transcripts to quickly troubleshoot customer issues. Learn how this service uses call and chat-specific analytics, including sentiment analysis and silence detection, to improve agents’ performance.

      Speaker: Sumit Patel, Enterprise Solutions Architect, AWS

    • AI/ML Services and Devices
    • AWS DeepRacer: Train, evaluate and tune your reinforcement learning model (Level 300)

      In this session, we introduce the basics of reinforcement learning and show you how to apply it train your own autonomous vehicle models. You also learn how to test them in a virtual car racing scenario powered by AWS DeepRacer. Learn about the single-car time-trial format and the dual-car head-to-head racing challenges in the AWS DeepRacer 3D racing simulator. At the end of this session, you will be able to participate in the AWS DeepRacer League, where you can compete for prizes and meet other machine learning enthusiasts.

      Speaker: Gabe Hollombe, Senior Developer Advocate, AWS

      Amazon CodeGuru: Automate code reviews and application performance recommendations (Level 400)

      It can be difficult to detect certain types of code issues and identify the most expensive lines of code without performance engineering expertise, even for the most seasoned engineers. CodeGuru is a new machine learning service that enables you to discover code issues quickly and improve application performance. Discover how CodeGuru works in this session. We show you how it reviews Java code in your GitHub and AWS CodeCommit source code repositories, profiles your applications, and searches for optimizations, even in production. Finally, learn how CodeGuru provides intelligent recommendations so that you can take immediate action to fix code issues or improve inefficiencies.

      Speakers:
      Pedro Paez, Specialist Solutions Architect, AWS
      Donnie Prakoso, Senior Developer Advocate, AWS

      Large scale image and video analysis with Amazon Rekognition (Level 200)

      Companies are using computer vision to understand the content and context of their images and videos at scale. This session provides an overview of Amazon Rekognition Custom Labels, a new feature of Amazon Rekognition that enables customers to build their own machine learning–based image analysis capabilities to detect unique objects and scenes that are relevant to their business needs. Join this session to learn how to use Amazon Rekognition Custom Labels for your own business needs. We also share how Amazon Augmented AI (Amazon A2I) makes it easy for human review of ML predictions and learn how worker safety is enabled with AWS DeepLens and Amazon Rekognition.

      Speaker: Imran Kashif, Senior Solutions Architect, AWS

      Get started with generative AI using AWS DeepComposer (Level 300)

      In this session, learn how to use generative AI to create music with AWS DeepComposer. Get hands on with the world's first machine learning enabled musical keyboard and record a melody. Then, learn how to send it to the cloud to generate an accompaniment. We demonstrate how to import the MIDI files into a digital audio workstation to create the final arrangement.

      Speaker: Julian Bright, AI Specialist Solutions Architect, AWS

    • Hands-on Labs
    • Reinforcement learning 

      In this lab, learn how to build reinforcement learning (RL) models using Amazon SageMaker RL, which includes prebuilt RL libraries and algorithms that help you achieve faster turnaround times and improve the results of your RL experiments.

      Customer churn prediction

      In this lab, learn how identifying unhappy customers early provides you with the opportunity to incentivize them to stay and helps decrease customer churn. In this lab, we explain how to use machine learning (ML) to predict customer churn. We also discuss how to incorporate the costs associated with prediction mistakes to determine the financial outcome of using ML.

      Virtual contact center

      This lab explains how to build a contact center using Amazon Connect and Amazon Lex. Learn how to match intent based on your input and provide greater flexibility for customers who interact with contact centers.

      AWS DeepRacer

      Get ready to race by building your own AWS DeepRacer reinforcement learning (RL) model. AWS DeepRacer is an integrated learning system for users of all levels that allows you to explore RL and experiment with building autonomous driving applications. In this lab, you get hands-on with creating, training, and tuning your RL model.

      Build, train, and debug machine learning models

      In this lab, we show the different aspects of the machine learning (ML) workflow for building, training, and deploying a model using all the capabilities of Amazon SageMaker. We also discuss how Amazon SageMaker removes the heavy lifting from each step of the ML workflow. Come learn how to build, train, debug, monitor, and deploy your ML models.

    • Builders' Zone Demo
    • Virtual rap battles

      AI is quickly making inroads into the entertainment industry, helping create music loops, drum track samples, and even entire heavy metal albums. In this session, learn how to use Amazon SageMaker to build and train a lyrics-generation model and host the model as a real-time API. We show you how to create two virtual rappers using Amazon Sumerian and have them trade rhymes with each other. For a control panel, we use Amazon Comprehend, which detects the key phrase from the first rapper and passes it to the second one and enables replies that are specific to the topic of the first rapper’s rhyme.

      Speaker: Tapan Hoskeri, Senior Solutions Architect, AISPL

      ML soccer bots

      AWS AI/ML and IoT services can be used together for more than solving edge use cases with limited internet connectivity. In this demo, watch as they make two bots smart enough to compete with each other—finding, chasing, and striking a soccer ball. These soccer bots are built using a single-board computer with a Pi camera as the only sensor. They come to life with a combination of AWS services like Amazon SageMaker and AWS IoT Greengrass Core. Observe how they each draw inferences locally from their vision data and make quick decisions using the ML models deployed on their single-board computer.

      Speakers:
      Arun Balaji, Partner Solution Architect, AISPL
      Ananth Balasubramanyam, Solutions Architect, AISPL

      Smart skittles sorter

      Modern connected industrial environments rely on various types of sensors and automation tools. Many such environments use advanced technologies like computer vision and machine learning for automation. In this session, we demonstrate how to use Amazon SageMaker and AWS IoT to deploy and operate a mechanism that sorts Skittles by color. Learn how to integrate, operate, and monitor industrial automation that consists of Raspberry Pi connected to a camera, relays, and motors and that uses AWS IoT and Amazon SageMaker.

      Speaker: Vijay Menon, Solutions Architect, AISPL

      Image classification with AWS DeepLens

      Learn how to build a custom deep learning image classification model with AWS DeepLens, Amazon SageMaker, AWS IoT Greengrass, and other AWS services. The model can be used for a variety of purposes, including artistic style transfer, facial recognition, and license plate and other object detection.

      Speaker: Ramine Tinati, Senior AI/ML Advocate, AWS

      Underwater garbage detection

      With an estimated 8 million metric tons of trash deposited into oceans each year, there are now close to 500 dead zones, where most marine life cannot survive, globally covering more than 245,000 square kilometers, equivalent to the area of the UK. Clearing this trash is a massive job requiring first knowing exactly where the trash is located. This demo shows how to use machine learning to detect trash underwater, mapping it to its location. We use services like Amazon SageMaker, Amazon Elasticsearch Service, and AWS IoT to run this model at the edge with TensorFlow and an NVIDIA Jetson AGX Xavier Developer Kit.

      Speakers:
      Kapil Pendse, Solutions Architect, AWS
      Janos Schwellach, Solutions Architect, AWS

      AI/ML blackjack challenge

      This AI/ML blackjack challenge demo showcases Amazon SageMaker, AWS IoT, and Serverless, using edge compute and cameras. Learn how these services in combination detect playing card rank and suit using computer vision, displaying results in real time, and provide player guidance recommendations and probabilities for the game of blackjack.

      Speaker: Ramine Tinati, Senior AI/ML Advocate, AWS

      Omni channel contact center

      The multichannel experience increasingly forms the core of the business strategy that organizations adopt to connect with their customers. While platforms such as web, mobile, and phone help companies engage and connect with their customers, in most cases, the customer still lacks a seamless experience and consistent messaging across these channels. This demo shows you how to build an omni-channel solution to serve customers in a way that creates an integrated and cohesive customer experience. Using the powerful conversational natural language capabilities of Amazon Lex, see how easily you can build voice-enabled chatbot experiences for customer service.

      Speaker: Donnie Prakoso, Senior Developer Advocate, AWS

      Intelligent car damage assessor

      When a motor vehicle insurance policyholder is involved in an accident, the claims agent ideally, instead of asking that customer to answer questions in a moment of stress, would ask them to simply take a few images of their vehicle and upload them into the customer’s mobile application. In this session, learn how to harness the power of machine learning to provide customers a hassle-free way to claim insurance damages—we cover training a machine learning model to assess damage to vehicles by simply analyzing uploaded images from customers so that you don’t have to rely on numerous subjective questions.

      Speaker: Anand Iyer, Principal Solutions Architect, AISPL 

      AI worker safety system

      Learn to use AWS DeepLens and Amazon Rekognition to build an application that helps identify if a person at a construction site is wearing the right safety gear, in this case, a hard hat. In this lab, we show how you can create and deploy an object detection project to AWS DeepLens, modify the AWS DeepLens object detection inference Lambda function to detect persons and upload the frame to Amazon S3, create a Lambda function to identify those who are not wearing safety hats and analyze the results using AWS IoT , Amazon CloudWatch and a web dashboard.

      Speaker: Imran Kashif, Senior Solutions Architect, AWS

Tingkat 100
Perkenalan
Sesi difokuskan pada menyediakan ikhtisar layanan dan fitur AWS, dengan asumsi bahwa peserta belum terlalu memahami topik ini.
Tingkat 200
Menengah
Sesi difokuskan pada menyediakan praktik terbaik, detail fitur layanan dan demo dengan asumsi bahwa peserta memiliki pengetahuan awal mengenai topik.
Tingkat 300
Lanjutan
Sesi mempelajari lebih dalam topik yang dipilih. Pembicara mengasumsikan bahwa peserta sudah cukup memahami topik, tetapi mungkin atau mungkin tidak memiliki pengalaman langsung dalam menerapkan solusi serupa.
Tingkat 400
Pakar
Sesi ditujukan untuk peserta yang sudah sangat memahami topik, pernah menerapkan solusi sendiri, dan tidak menemui kesulitan dalam cara kerja teknologi di beberapa layanan, arsitektur, dan penerapan.

Pembicara Utama

Glenn Gore
Glenn Gore, Chief Architect, AWS

Sebagai chief architect untuk AWS, Glenn bertanggung jawab atas pembuatan praktik terbaik dari sisi arsitektur dan bekerja sama dengan pelanggan mengenai bagaimana mereka menggunakan cloud dan inovasi untuk melakukan transformasi bisnis mereka sendiri atau mengguncang pasar baru.

Glenn sebelumnya memegang peran lain di AWS, yang terbaru sebagai head of architecture untuk Asia Pasifik dan EMEA, dan mengelola tim regional yang bekerja dalam dua wilayah yang tumbuh paling cepat. Glenn adalah seorang pakar teknologi yang senang terlibat dengan kustomer dengan pengalaman lebih dari 20 tahun di industri teknologi. Sebelum bergabung dengan AWS, Glenn menjadi CTO WebCentral, tempatnya mengerjakan platform web yang sangat dapat diskalakan dan sistem big data untuk pelanggan. Ia juga pernah bekerja di OzEmail dan UUNET, penyedia jaringan terbesar di dunia.

Olivier Klein
Olivier Klein, Head of Emerging Technologies, AWS

Olivier adalah seorang ahli teknologi yang senang terlibat dengan kustomer, berpengalaman lebih dari 10 tahun di industri dan pernah bekerja untuk AWS di APAC dan Eropa untuk membantu pelanggan membangun aplikasi yang tangguh, dengan skalabilitas yang tinggi, aman, dan hemat biaya, serta membuat model bisnis yang inovatif dan didukung oleh data. Ia berbagi bagaimana teknologi yang sedang berkembang dalam lingkup kecerdasan buatan, machine learning, dan IoT dapat membantu menciptakan produk baru, membuat proses yang sudah ada semakin efisien, menyediakan wawasan bisnis menyeluruh, dan memanfaatkan saluran keterlibatan baru untuk pelanggan. Ia juga secara aktif membantu pelanggan membangun plaftorm yang menyelaraskan infrastruktur IT, secara efektif meningkatkan efisiensi dan mengguncang proses pengembangan produk yang telah dijalankan selama beberapa dekade sebelumnya.

Dean Samuels
Dean Samuels, Lead Architect, AWS

Dean datang dari latar belakang infrastruktur IT dan memiliki pengalaman luas dalam virtualisasi dan automasi infrastruktur. Dean telah lima tahun bekerja di AWS dan pernah mendapat kesempatan untuk bekerja dengan bisnis dari segala ukuran dan industri, terutama di Australia dan Selandia Baru, juga di wilayah APAC yang lebih luas. Dean berkomitmen membantu pelanggan mendesain, menerapkan, dan mengoptimalkan lingkungan aplikasinya untuk cloud publik agar dapat menjadi lebih inovatif, tangkas, dan aman. Meski memiliki latar belakang kuat dalam infrastruktur IT yang mencakup komputasi, penyimpanan, jaringan, dan keamanan, fokus utama Dean adalah pada memadukan praktik operasional IT dan pengembangan perangkat lunak dengan cara yang lebih kolaboratif dan terintegrasi.

FAQ

1. Berapa biaya untuk menghadiri AWS Innovate?
2. Siapa yang sebaiknya menghadiri AWS Innovate?
3. Apakah saya bisa mendapatkan konfirmasi untuk pendaftaran AWS Innovate saya?
4. Apakah ada sesi dalam bahasa lain?
5. Bagaimana saya dapat menghubungi penyelenggara konferensi online?

T: Berapa biaya untuk menghadiri AWS Innovate?
J: AWS Innovate adalah konferensi online gratis.

T: Siapa yang sebaiknya menghadiri AWS Innovate?
J: Baik baru menggunakan AWS maupun pengguna yang berpengalaman, Anda dapat mempelajari hal baru di AWS Innovate. AWS Innovate dirancang untuk mengembangkan keterampilan yang tepat untuk membuat wawasan baru, memungkinkan efisiensi baru, dan membuat prediksi yang lebih akurat.

T: Apakah saya bisa mendapatkan konfirmasi untuk pendaftaran AWS Innovate saya?
J: Setelah penyelesaian proses pendaftaran, Anda akan menerima email konfirmasi.

T: Apakah ada sesi dalam bahasa lain?
J: Kami memiliki sesi dalam bahasa Korea, Bahasa Indonesia, Mandarin, Portugis, dan Spanyol.

T: Bagaimana saya dapat menghubungi penyelenggara konferensi online?
T: Jika memiliki pertanyaan yang belum dijawab di FAQ di atas, silakan kirim email kepada kami.

 


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