AWS Innovate AI/ML Edition
Percepat inovasi, skalakan dengan mudah, dan buka berbagai peluang baru dengan machine learning (ML) di AWS.
 Rabu, 24 Februari 2021

50+

Sesi
Berdiskusi
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Pelanggan
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Panduan

Agenda

Dapatkan inspirasi dan pelajari cara menggunakan machine learning untuk mendorong pengalaman yang lebih baik, menyederhanakan operasi, dan mengurangi risiko, serta memperoleh kemampuan untuk mengimplementasikan proyek-proyek ini bagi organisasi Anda.

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Pilih Trek:

  • Sesi Bahasa Indonesia
  • English Sessions
  • Sesi Bahasa Indonesia
  • Strategi untuk mempercepat adopsi AI/ML dalam skala besar: Dari ide hingga POC dan mencapai hasil bisnis (Tingkat 100)
    AI dan ML memiliki janji untuk mengubah industri, meningkatkan efisiensi, dan mendorong inovasi. Kunci kesuksesan machine learning adalah skala. Dalam sesi ini, kami membahas bagaimana para eksekutif dan manajer yang ingin mencapai kesuksesan menggunakan ML dalam skala besar mendapatkan panduan termasuk mekanisme untuk membangun sistem yang efektif untuk mempercepat inovasi dan mendorong kemajuan teknologi. Kami juga membagikan praktik terbaik dalam menerapkan MLOps dan tata kelola data untuk mengatasi tantangan penerapan ML. Kami menjelaskan bagaimana pelanggan yang telah sukses bekerja bersama kami untuk menyelaraskan tim dalam memperkenalkan ML, mendorong semangat ML, dan memberikan pendidikan teknis yang tepat kepada pengembang dalam organisasi mereka untuk mencapai hasil bisnis.

    Pembicara: Donnie Prakoso, Senior Developer Advocate, AWS


    Tingkatkan keterlibatan dan konversi pelanggan dengan pengalaman pengguna yang dipersonalisasi (Tingkat 200)
    Seiring dengan berkembangnya kemampuan untuk menghadirkan pengalaman digital yang lebih canggih dari waktu ke waktu, ada peningkatan harapan dan permintaan dari pelanggan untuk mendapatkan pengalaman yang lebih personal dari produk-produk yang mereka gunakan. Saat pelanggan mempertimbangkan untuk membeli produk dan jasa, mereka mengharapkan personalisasi secara real-time dari berbagai kanal digital. Dalam sesi ini, kami akan menceritakan dengan detail tentang penggunaan Amazon Personalize untuk membuat dan mengelola rekomendasi yang dipersonalisasi secara efisien, sehingga memungkinkan Anda untuk fokus pada nilai nyata dari data untuk bisnis Anda. Mari pelajari cara membangun aplikasi yang mampu memberikan beragam pengalaman yang dipersonalisasi, termasuk rekomendasi produk tertentu, pengurutan produk yang dipersonalisasi, dan pemasaran langsung yang dipersonalisasi - tanpa memerlukan kemampuan Machine Learning (ML).

    Pembicara: Teddy Aryono, Solutions Architect, AWS


    Membuat dan mengelola set data pelatihan untuk pembelajaran mesin (Tingkat 200)
    Mempersiapkan data pelatihan adalah langkah penting dalam machine learning. Mempersiapkan data melibatkan pembuatan data berlabel, pembuatan fitur, visualisasi fitur, dan pemrosesan data sehingga dapat tersedia untuk pelatihan. Dalam sesi ini, pelajari cara menggunakan SageMaker Data Wrangler untuk terhubung ke sumber data, menggunakan template visualisasi prebuilt dan data built-in untuk mengubah dan menyederhanakan proses pembersihan, verifikasi, dan eksplorasi data tanpa harus menulis satu baris kode pun. Dalam sesi ini, kami memberikan demonstrasi tentang bagaimana SageMaker Data Wrangler mempublikasikan data ke penyimpanan Fitur SageMaker dan menjelaskan cara menggunakan alur kerja persiapan data ke dalam produksi menggunakan SageMaker Pipelines.

    Pembicara: Petra Barus, Senior Developer Advocate, AWS


    Memilih algoritme ML yang tepat untuk berbagai kasus penggunaan (Tingkat 300)
    AWS menawarkan banyak pilihan untuk menyelesaikan masalah bisnis melalui machine learning (ML), mulai dari algoritme bawaan hingga kerangka kerja dan lainnya dalam menggunakan layanan ML. Amazon SageMaker mendukung berbagai algoritme ML bawaan, seperti klasifikasi, regresi, dan rekomendasi. Algoritme bawaan mudah digunakan, dan dioptimalkan untuk kecepatan, skala, dan akurasi. Dalam sesi ini, pelajari cara memilih algoritme bawaan yang tepat untuk masalah bisnis Anda. Sesi ini mengkategorikan algoritme ini berdasarkan jenis masalah dan mendalami algoritme populer. Bawalah rasa ingin tahu Anda dan tinggalkan informasi yang Anda butuhkan untuk memilih algoritme bawaan yang tepat untuk kebutuhan bisnis Anda.

    Pembicara: Petra Barus, Senior Developer Advocate, AWS


    10 hal yang perlu diketahui oleh Startup tentang AWS AI/ML (Tingkat 100)
    Apakah Anda pernah bertanya-tanya bagaimana ilmuwan data di pasar pengiriman digital Convoy dapat menemukan rute angkutan truk yang paling hemat biaya atau bagaimana startup perawatan kesehatan SyntheticGestalt mengambil proses penemuan obat dari 4 tahun menjadi 9 jam untuk mempercepat penelitian ilmiah? Mereka adalah pengguna awal Amazon SageMaker, layanan pembelajaran mesin Amazon yang memungkinkan Anda membuat, melatih, dan menerapkan model ML dengan cepat. Dalam sesi ini, pelajari praktik terbaik dan 10 hal terpenting yang dibutuhkan startup untuk mempercepat perjalanan AI/ML mereka.

    Pembicara: Fitria, Startup Inside Sales Representative, AWS

  • English Sessions
    • Keynote
    • AI/ML: Solving the big issues
    • Accelerate AI/ML journey
    • Use cases of AI services
    • Prepare, build, train, deploy ML models
    • AI/ML fundamentals
    • AI/ML for Startups
    • Integration with ML
    • AWS DeepRacer
    • Hands-on labs
    • Builders Zone
    • Keynote
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      Keynote

      Building a smarter, faster business using AI/ML on AWS (Level 100)

      In 2021, businesses across all industries will have opportunities to build deeper relationships with their customers, run more efficient operations, and pivot with new innovations than any year before. A major enabler of these is the accessibility of AI/ML for all types of organizations. Over the last year, AWS released several new services and features to help organizations launch more quickly, lower the technical bar to get started with AI/ML, and to reduce friction for hybrid and diverse platforms.

      Speakers
      Craig Stires, Head of AI and Machine Learning Sales Lead, APJ, AWS
      Olivier Klein, Lead Technologist, APJ, AWS

      Duration: 50mins

    • AI/ML: Solving the big issues
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      AI/ML: Solving the big issues

      About the track

      Hear from executives across various industries as they share what they have learnt and experimented with over the past months using agile technology, upskilling employees, and driving a culture of innovation to build for the future. You can also get insights into how Amazon.com uses AI/ML to personalize, scale, and invent new and compelling customer experiences.

      Innovation is never normal (Level 100)
      In 2020, the phrase ‘never normal’ became common language. And like most periods of major upheaval, the first instinct of some leaders is to focus on survival. For businesses working with AI and ML, however, living this never normal is simply ‘business as usual’, where constant change offers abundant opportunities to innovate and thrive.

      Join Olivier Klein, Lead Architect, APJ, AWS as he presents the following customer stories.

      Customer story #1: Bowery Farming – The future of food production
      In the face of increasingly challenged global food supply chains, and the need to find more sustainable food production practices, Bowery Farming has turned to technology innovation to increase annual crop yields by a hundred times, using a fraction of the resources needed in traditional farming.
      Speaker: Henry Sztul, Executive Vice President of Science and Technology, Bowery Farming

      Customer story #2: Soul Machines – The digital humans enhancing customer experience
      Soul Machines brings the ‘human touch’ to transform customer and brand experiences, where machines manage repetitive and simple tasks so that real humans can manage the complex ones. Using a patented ‘Digital Brain’ its digital people contextualize customer interactions, adapting in real time in a similar way to actual human beings.
      Speaker: Greg Cross, Founder and CBO, Soul Machines

      Customer story #3: Transfix – Freight logistics transformed in a digital marketplace
      Leading tech start-up, Transfix, is hauling the $800 Billion US trucking industry into the 21st century to better match and connect shippers with carriers. Its AI and ML-based, digital freight marketplace ensures fairer pricing, increased trust and reliable service level agreements.
      Speakers: 
      Lily Shen, President and Chief Operating Officer
      Jonathan Salama, Co-founder and Chief Technology Officer, Transfix

      Customer story #4: University of Sydney – Protecting endangered species with AI and ML
      Preserving species diversity is vital to the future health of the planet and Australia is at the forefront of this challenge, with endangered flora and fauna species numbering in the thousands. Dr Carolyn Hogg and the team at the Australasian Wildlife Genomics Group, University of Sydney, use data science to accelerate the sequencing of Genomic data to save time, maximize conservation dollars and save beloved animals like the Tasmanian Devil.
      Speaker: Dr. Carolyn Hogg, Australasian Wildlife Genomics Group, University of Sydney

      To conclude the session, join Pradeep K. Dubey, Intel Senior Fellow and Director of the Parallel Computing Lab, and Olivier Klein, Lead Architect, AWS, as they discuss the technology advances that have allowed AI to move from simple number crunching to making decisions. The ability of AI to help better predict future long tail, or Black Swan events such as COVID-19 is also explored.
      Speaker: Pradeep K. Dubey, Intel Senior Fellow and Director of the Parallel Computing Lab

      Duration:
      45mins


      Driving innovation at Amazon (Level 100)
      At Amazon, everyone wants to innovate fast for customers. Many of our best ideas come ground up, from the people closest to customers. This session takes you behind-the-scenes to see how Amazon uses AI/ML to personalize, scale and invent new and compelling customer experiences. Focusing on insights gained and lessons learned, the session will cover the cultural, process, and technology aspects of building and scaling AI and Machine Learning capabilities across the organization.

      Amazon.com enhancing CX
      From its inception, Amazon.com's consumer retail business has transformed the shopping experience, from product search through to customer delivery. In this session, we share specific examples from Amazon.com's consumer retail businesses to demonstrate how AI & ML helps Amazon deliver the optimum customer experience, improves efficiency, and lowers the cost to serve.
      Speaker: Choong Lee, Strategic Business Development Manager, AWS

      From call center operator to brand ambassador
      Most of us can relate to the frustration a caller experiences with disproportionately long phone cues, and the need to repeat personal details time and again when seeking customer support to resolve a product or service issue. For customer services operators, the experience can be equally challenging. This session focuses on why Amazon.com decided to build Connect to serve millions of people daily and ensure personalized, dynamic, and positive customer experiences for all.
      Speakers:
      Scott Brown, Head of Worldwide GTM, Productivity Applications, AWS
      Yasser El-Haggan, Head of Worldwide Solutions Architecture, Productivity Applications, AWS

      Amazon Connect - From ‘Lift ‘n Shift’ to CX innovation
      More than a simple call centre technology stack, Amazon Connect offers a range of features that move customer service from reactive to proactive through innovations such as text to speech and highly contextualized customer data analysis to help all businesses create positive and compelling customer experiences.

      Speakers:
      Scott Brown, Head of Worldwide GTM, Productivity Applications, AWS
      Yasser El-Haggan, Head of Worldwide Solutions Architecture, Productivity Applications, AWS

      Duration:
      45mins

    • Accelerate AI/ML journey
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      Accelerate AI/ML journey

      About the track

      Provides customers with insights on how AI & ML inspires business innovation, transforms customer experiences and improves business outcomes. Whether it’s enhancing customer experiences, creating advanced real-time recommendations, accelerating new product development, boosting employee productivity to cutting costs and reducing fraud, organizations today are using AI and ML to solve business challenges and innovate faster.

      Strategies to accelerate AI/ML at scale: From idea to POC and achieve business outcomes (Level 100)
      AI and ML hold the promise of transforming industries, increasing efficiencies, and driving innovation. The key to machine learning success is scale. In this session, we cover how executives and managers who are looking to achieve success using ML at scale get guidance including mechanisms to build an effective system to accelerate innovation and drive technological progress. We also share best practices in implementing MLOps and data governance to overcome ML implementation challenges. We explain how customers who have been successful are working with us to align teams in introducing ML, driving ML excitement, and providing developers within their organization the right technical education to achieve business outcomes.

      Speakers: 
      Bernard Leong, Head of Machine Learning and Artificial Intelligence, ASEAN, AWS
      Chris Howard, Head of AI/ML Solutions Architecture, APJ, AWS
      Duration: 60mins

    • Use cases of AI services
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      Use cases of AI services

      About the track

      Learn how AWS AI services are applied to applications and used in real-life use cases. We focus on how AI services can easily integrate with applications to address common use cases such as personalized recommendations, modernizing contact center, improving safety and security, and increasing customer engagement with no machine learning skills required. 

      Building intelligence into the contact center (Level 300)
      Your contact center is the biggest touchpoint between you and your customers, and every engagement can provide your team with powerful insights. In this session, we show how to leverage the new capabilities in Amazon Connect such as Contact Lens to transcribe calls, do sentiment analysis, and surface valuable customer insights from every conversation powered with machine learning. Learn how to discover emerging themes and trends from customer conversations in real-time and allow you to respond faster and serve your customers better. We showcase how you can integrate other AWS ML services such as Amazon Personalize with Amazon Connect and help with predicting intent by leveraging the data you have, thereby creating intelligent and personalized experiences for your customers. 

      Speaker: Sumit Patel, Enterprise Architect, AWS
      Duration: 30mins


      Intelligent document processing: Building higher accuracy document automation at scale (Level 200)
      Organizations have millions of physical documents and forms that hold critical business data. These documents, such as insurance claims or loan applications, have structured and unstructured data that are either extracted by humans or by rule-based systems which are not easily scalable, have high costs, and could produce low-accuracy extraction results. In this session, learn how to use Amazon Textract, Amazon Comprehend, and Amazon Augmented A2I to extract structured data, redact sensitive information, and deploy your automated document processing workflow into production, at scale. 

      Speaker: Jonathan Headley, Principal AI/ML Specialist Solutions Architect, AWS
      Duration: 30mins


      Improve customer engagement and conversion with personalized user experiences (Level 200)
      As the ability to deliver more sophisticated digital experiences evolve over time, the expectation and demand from customers to receive a more personalized experience from companies and products they engage with have also increased. Customers today expect real-time, curated experiences across digital channels as they consider, purchase, and use products and services. In this session, we deep-dive into using Amazon Personalize to create and manage personalized recommendations efficiently, letting you focus on the real value of the data for your business. Learn how to build applications capable of delivering a wide array of personalized experiences, including specific product recommendations, personalized product re-ranking, and customized direct marketing - with no ML experience required. 

      Speaker: Alex Thewsey, AI/ML Specialist Solutions Architect, AWS 
      Duration: 30mins


      Create smarter bots with intelligent search and improve customer satisfaction (Level 200)
      Customer service conversations typically revolve around one or more topics and contain related questions. Answering these questions seamlessly is essential for a good conversational experience. In this session, learn how you can build an intelligent bot with Amazon Lex and integrate it with Amazon Kendra. Amazon Kendra provides you with a highly accurate and easy-to-use enterprise search service powered by machine learning. It offers a more intuitive way to search—using natural language—and returns more accurate answers. You simply point Amazon Kendra at your content, and Amazon Kendra indexes the content to provide the answers. With this solution, customers get a response right away, and support staff can focus on solving problems and improving customer satisfaction.

      Speaker: Sara van de Moosdijk, ML Specialist Solutions Architect, AWS
      Duration: 30mins


      Detect more online fraud faster (Level 200)
      Globally each year, organizations lose tens of billions of dollars to online fraud. Amazon Fraud Detector is a fully managed AI service that uses machine learning (ML) and more than 20 years of fraud detection expertise from Amazon to identify potentially fraudulent activities so that customers can catch more online fraud faster. Learn how Amazon fraud mitigation strategies influenced the development of Amazon Fraud Detector, and hear how organizations are applying it to prevent and detect online fraud, and deliver successful outcomes.

      Speaker: Eric Greene, Senior AI/ML Specialist Solutions Architect, AWS
      Duration: 30mins


      Using AI to streamline media content operations (Level 200)
      Reviewing, searching, and analyzing image and video content at scale remains a top challenge for media and entertainment organizations. Learn how organizations are using Amazon Rekognition and Amazon Rekognition Custom Labels to get more out of their content archives.

      Speaker: Alastair Cousins, Principal Solutions Architect, AWS
      Duration: 30mins

    • Prepare, build, train, deploy ML models
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      Prepare, build, train, deploy ML models

      About the track

      Learn how to easily build custom trained ML models with existing algorithms or pre-trained models. Understand best practices to decide what, where, and how when putting ML solutions into production, illustrate on what the model is and what the business context is, as well as where to deploy and how to deploy. This track also includes working backwards from customer questions, and implementing and scaling ML models with MLOps.

      Jumpstart to prepare, build, train, and deploy ML models on AWS (Level 200)
      Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. In this session, we provide an overview for one of the fastest growing services in AWS history. Amazon SageMaker is built on Amazon’s two decades of experience developing real-world machine learning applications, including product recommendations, personalization, intelligent shopping, robotics, and voice-assisted devices. Learn how to prepare, build, train, tune, deploy, and manage your first machine learning model on AWS.

      Speaker: Tapan Hoskeri, Solutions Architect, AISPL
      Duration: 30mins


      Build and manage training datasets for machine learning (Level 200)
      Preparing training data is a critical step in machine learning. Preparing data involves creating labelled data, creating features, visualizing the features, and processing the data so it can be made available for training. In this session, learn how to use SageMaker Data Wrangler to connect to the data sources, use prebuilt visualization templates and built-in data to transform and streamline the process of cleaning, verifying, and exploring data without having to write a single line of code. In this session, we provide a demonstration of how SageMaker Data Wrangler publishes the data to SageMaker Feature store and explain how to take data preparation workflows into production using SageMaker Pipelines. 

      Speaker: Praveen Jayakumar, Principal Solutions Architect, AI/ML, AISPL
      Duration: 30mins


      Incorporating explainability and fairness-awareness in ML solutions (Level 300)
      Machine learned models and data-driven systems are being increasingly used to help make decisions in application domains such as financial services, healthcare, education, and human resources. With the goal that a significant portion of these decision systems becoming fully-automated, there is need for understanding and rectifying the underlying bias in data, algorithms, and objectives, including providing reliable explanations for the predictions and decisions taken by these machine learning (ML) systems. In this session, we share how Amazon SageMaker Clarify and SageMaker Debugger address some of the regulatory, business and data science questions that arise in the context of explainability and fairness in ML. We would also specifically dive deep into how builders can incorporate these best practices in explainable and fairness-aware ML into their solutions using these AWS services.

      Speaker: Sujoy Roy, Senior Data Scientist, AWS
      Duration: 30mins


      Build enterprise scale ML workflows on Kubernetes and Amazon SageMaker with Kubeflow (Level 200)
      Until recently, data scientists had to spend significant time performing operational tasks, such as ensuring frameworks, runtimes, and drivers for CPUs and GPUs are working well together. They are 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. Kubeflow Pipelines is an add-on to Kubeflow that allows you to build and deploy portable and scalable end-to-end ML workflows. In this session, learn how you can integrate Amazon SageMaker features such as data labeling, large-scale hyperparameter tuning, distributed training jobs, and secure scalable model deployment using SageMaker Components for Kubeflow Pipelines.

      Speaker: KJ Pittl, ISV Solutions Architect, AWS
      Duration: 30mins


      DIY and fully-managed ML deployments on AWS (Level 200)
      AWS offers and delivers the broadest choice of powerful compute, high speed networking, and scalable high-performance storage options for any machine learning (ML) project or application. You can also choose the ML infrastructure for a do-it-yourself approach or implement a fully managed approach with Amazon SageMaker. In this session, we explore how to deploy your inference models on AWS, what factors to consider and how to optimize the deployments. We share best practices and approaches to get your ML workloads running smoothly and efficiently on AWS.

      Speaker: Eshaan Anand, Senior Partner Solutions Architect, AWS
      Duration: 30mins


      Accelerating machine learning innovation securely with Amazon SageMaker (Level 200)
      To build successful machine learning models you need datasets unique to your organization. These datasets are extremely valuable assets and need to be secured throughout every step of the machine learning process. In a typical machine learning project it can take months to build a secure workflow before you can begin any work on your models. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly and securely. In this session, we provide an overview of the Amazon SageMaker security features that help organization meet the strict security requirements of machine learning workloads.

      Speaker: Michael Stringer, Senior Solutions Architect, AWS
      Duration: 30mins

    • AI/ML fundamentals
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      AI/ML fundamentals

      About the track

      This track features new AI/ML announcements that will excite the developers. We share how to design and build machine learning (ML) pipelines to orchestrate complex workflows when deploying ML models. 

      Building and orchestrating serverless ML workflows (Level 300)
      Machine learning (ML) workflows can be orchestrated with Amazon SageMaker and AWS Step Functions. With Amazon SageMaker, you can build, train, and deploy ML models quickly and easily at scale. With Step Functions, you can add resilient serverless workflows to your applications. Workflows on Step Functions require less code to write and maintain. In this session, we combine the best of both worlds by using AWS Step Functions to automate and orchestrate ML workflows with Amazon SageMaker for an end-to-end experience for developers. We share how AWS Step Functions helps in monitoring SageMaker jobs while enabling a seamless experience for your workflows.

      Speaker: Julian Bright, Senior AI/ML Specialist Solutions Architect, AWS
      Duration: 30mins


      Automate code reviews, performance recommendations, and operational insights (Level 300)
      A better understanding of your code base helps reduce overall costs, improves non-functional behaviors like application response times and performance, and allows you to tackle issues faster and more accurately. Similarly, from the operational front, it can be difficult to identify operational issues long before they impact your customers. In this session, learn more about Amazon CodeGuru, a developer tool for automating code reviews to detects issues such as deadlocks, data races on thread unsafe classes, atomicity violations and over-synchronization related to concurrency bugs. The session also includes automating performance reviews through application profiling, identify lines of expensive object recreation, usage of inefficient libraries, logging and concurrency issues that improves code performance for applications in production. In addition, the session covers Amazon DevOps Guru which makes it easier for developers and operators to automatically detect operational issues and recommend options for remediation or mitigation that improves overall applications availability, operational performance and insights while reducing expensive downtime.

      Speaker: Aashmeet Kalra, Senior Solutions Architect, AWS
      Duration: 30mins


      Choosing the right ML algorithms for the different use cases (Level 300)
      AWS offers many choices for solving business problems through machine learning (ML), ranging from built-in algorithms to frameworks and more in using ML services. Amazon SageMaker supports the different built-in ML algorithms, such as classification, regression, and recommendation. Built-in algorithms are easy to use, and they are optimized for speed, scale, and accuracy. In this session, learn how to choose the right built-in algorithm for your business problem. This session categorizes these algorithms by problem types and dives deep into popular ones. Bring your curiosity and walk away with the information you need to choose the right built-in algorithm for your business requirements.

      Speaker: Pedro Paez, Senior AI/ML Specialist Solutions Architect, AWS
      Duration: 30mins

    • AI/ML for Startups
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      AI/ML for Startup

      About the track

      Learn how Startups can easily leverage AWS AI/ML stack to quickly and painlessly build their Startups. Understand best practices, new product launches and hacks to build a cost effective and scalable ML solution on AWS. Learn from experienced founders how you can easily avoid common pitfalls.

      10 things Startups need to know about AWS AI/ML (Level 100)
      Ever wonder how data scientists at digital freight marketplace Convoy are able to find the most cost-efficient trucking routes or how healthcare Startup SyntheticGestalt is taking the drug discovery process from 4 years down to 9 hours to accelerate scientific research? They were early adopters of Amazon SageMaker, Amazon’s machine learning service that lets you quickly build, train, and deploy ML models. In this session, learn best practices and the top 10 things Startups need to accelerate their AI/ML journey.

      Duration: 30mins


      Scaling your Startup: How to build an incredible business by leveraging existing tools (Level 200)
      AWS provides ML services for every use case so Startups of any size can launch immediately. AWS is among the top rated on Stanford’s 2020 deep learning benchmark, DAWNBench, for the fastest training time, lowest cost, lowest inference latency, and deepest set of security features. In this session, learn how Startups can easily leverage AWS AI/ML stack to quickly and painlessly build their business. Understand best practices, new product launches, and hacks to build a cost-effective and scalable ML solution on AWS.

      Duration: 30mins


      Confessions of AI/ML Startup founders (Level 100)
      Hear from Startups founders as they share how they use AWS AI/ML services to unlock new possibilities and deliver business outcomes at scale. At the same time learn from these experienced founders on how you can easily avoid common pitfalls for your Startup. 

      Duration: 30mins

    • Integration with ML
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      Integration with ML

      About the track

      Learn about the various ML integrations available that can help you build, train, and deploy your ML models efficiently and at scale.

      MLOps for edge devices with Amazon SageMaker Edge Manager (Level 200)
      In this session, learn more about Amazon SageMaker Edge Manager, a new capability of Amazon SageMaker that helps developers operate machine learning (ML) models on a fleet of edge devices and solve challenges with constraints and maintenance of ML models on edge devices. Find out how you can use Amazon SageMaker Edge Manager to build an MLOps pipeline from the cloud to the edge and back by preparing multiple variants of a model, including deployment on different edge devices, monitoring models deployed across a fleet, capturing data samples from each model instance on each device, and sending data securely from the fleet to the cloud for labeling and retraining with SageMaker.

      Speaker: Kapil Pendse, Senior Solutions Architect, AWS
      Duration: 30mins


      Accelerate innovation and ML workloads using your data with Amazon FSx for Lustre and Amazon S3 (Level 200)
      Organizations have accumulated massive amounts of data, and are continuing to accumulate data which can be consumed for machine learning. With all that stored data, how can customers easily leverage the value of their data to accelerate their machine learning workloads? In this session, we focus on showing how you can seamlessly leverage Amazon FSx for Lustre and Amazon S3, whether it is with a compute fleet or Amazon SageMaker to supercharge your workloads to accelerate business outcomes.

      Speaker: Wali Akbari, Senior APJ Storage Solutions Architect, AWS
      Duration: 30mins


      Create, train, and deploy machine learning (ML) models using familiar SQL commands (Level 200)
      Using data in your data warehouse for machine learning use cases like churn prediction can be complicated because of the different tools and skills required. In this session, learn how with Amazon Redshift Machine Learning, you can use SQL to automatically create, train, and apply machine learning (ML) models with the data in your data warehouse using familiar SQL commands. Join this session to learn how to leverage this new Amazon SageMaker integration to embed predictions like fraud detection and risk scoring directly in queries and reports, without any prior ML experience. 

      Speaker: Suman Debnath, Principal Developer Advocate, AWS 
      Duration: 30mins

    • AWS DeepRacer
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      AWS DeepRacer

      About the track

      Compete for prizes and meet fellow machine learning enthusiasts, online. Racers will have the opportunity to join the DeepRacer online session and have a 1-to-1 chat with our machine-learning experts.

      Get rolling with machine learning on AWS DeepRacer (Level 200)
      Developers, start your engines! This session provides developers of all skill levels an opportunity to get hands-on experience with AWS DeepRacer and hear about exciting announcements and enhancements coming to the league in 2021. Learn about the basics of machine learning and reinforcement learning (a machine learning technique ideal for autonomous driving). In this session, you can build a reinforcement learning model and submit it to the AWS DeepRacer League for a chance to win prizes and glory.

      Speaker: Janos Schwellach, Specialist SA Developer, AWS
      Duration: 30mins


      Shift your ML model into overdrive with AWS DeepRacer analysis tools (Level 300)
      Make your way from the middle of the pack to the top of the AWS DeepRacer podium. Once you have built your first reinforcement learning model, extend your machine learning skills in this session by exploring how human analysis of reinforcement learning through logs improves your performance through trend identification and optimization to better prepare for the races. 

      Speaker: Donnie Prakoso, Senior Developer Advocate, AWS
      Duration: 30mins

    • Hands-on labs
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      Hands-on labs

      About the track

      Learn from a series of hands-on labs, and chat with our AWS experts and understand how to get started, get certified and build your own learning path moving forward.

      Personalized recommendations (Level 200)
      In this lab, learn the basics of how to use Amazon Personalize in order to create a recommendation system. Amazon Personalize is a service which is based off the same technology used at Amazon.com. It is designed for users who would like to have a managed recommendation engine, but may not have the experience required to build their own.


      Virtual contact center (Level 200)
      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. Explore a set of capabilities for Amazon Connect enabled by machine learning (ML) that gives contact center supervisors and analysts the ability to understand the content, sentiment, and trends of their customer conversations.


      Enterprise search with Amazon Kendra (Level 200)
      In this lab, we demonstrate using Amazon Kendra to setup our own Enterprise Search instance, index HTML/PDF content in Amazon S3, and use a variety of query types to return accurate search results for end users.


      Customer churn prediction (Level 200)
      In this lab, learn how identifying unhappy customers early provides you with the opportunity to incentivize them to stay and helps decrease customer churn. 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.


      Automating code review with Amazon CodeGuru Reviewer (Level 200)
      In this lab, we walk you through how to associate Amazon CodeGuru with your repo and automate code review using Amazon CodeGuru Reviewer.


      Build, train, and debug machine learning models (Level 200)
      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.


      Sentiment analysis web app (Level 200)
      In this lab, we demonstrate how to add AI and ML cloud services features to your web application with React and the Amplify Framework.


      AWS DeepRacer (Level 200)
      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.

    • Builders Zone
    • builder-zone-icon-v2

      Builders Zone

      About the track

      Dive deep into technical stacks, learn how AWS experts have helped solve real-world problems for customers, try out these demos with step-by-step guides, and walk away with the ability to implement these or similar solutions in your own organization.

      Worker safety system using customized image and video analysis (Level 300)
      Learn how 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 session, 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, Principal Solutions Architect - Amazon AI, AWS


      Multilingual omnichannel contact center (Level 300)
      This project recognizes the contact center industry’s critical language barrier issue between agents and customers participating in a live chat conversation. It would perform real-time translation of the chat conversation between the agent and the customer and provide a chat-based output to both sides according to their desired languages (the team is also performing research to try this solution for voice-based conversations). The project leverages Amazon Connect to provide a seamless contact center experience, as well as Amazon Translate, Amazon Polly, and Amazon Transcribe.

      Speakers:
      Jackysh Bangera, Solutions Architect, AWS
      Gaurav Sahi, Principal Solutions Architect, AWS


      Automated corrosion detection using machine learning (Level 300)
      Visual inspection of industrial environments is a common requirement across heavy industries, and as a result, experts often have to perform manual inspections in adverse environments that put them at risk. Many of these industries deal with huge metal surfaces that are subject to corrosion, which poses a serious risk and huge financial impact. This demonstration showcases a machine learning approach to corrosion detection that helps visualize corroded areas. Learn how AWS Step Functions is used to create Amazon SageMaker machine learning models and deploy them for inference within a web application built with AWS Amplify and Amazon CloudFront.

      Speakers:
      Aravind Kodandaramaiah, Global Accounts Solutions Architect - Prototyping, AWS
      Mehdi Far, Senior Solutions Architect - AI/ML, AWS


      Garbage underwater detection (Level 300)
      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


      Sign and Speak (Level 300)
      This demo showcases how the Sign & Speak program uses machine learning (ML) to build a tool that facilitates communication between users of sign language and users of spoken language. By combining artificial intelligence (AI) models trained to transcribe speech and interpret sign language with a camera and a microphone, the tool enables two-way conversation in situations where communication was previously challenging.

      Speaker: Eshaan Anand, Senior Partner Solutions Architect, AWS


      Creating intelligent digital twin for physical industrial equipment (Level 200)
      Learn how the digital twin provides a near real-time view of the physical asset, built using Amazon Sumerian to address predictive maintenance, failure prediction, and discharge pressure analysis using AWS AI and ML services. We show how the digital twin leverages Amazon Sumerian, Amazon SageMaker, AWS Lambda and Amazon QuickSight to detect issues before they occur, prevent downtime, develop new opportunities, and plan ahead by using simulations. We also demonstrate how to build a 3D digital twin for physical equipment like power fluid pump using AWS.

      Speaker: Harjot Kalra Enterprise Solutions Architect, AISPL


      Build an intelligent marketing kiosk (Level 200)
      In this demo, we show how to build a smart ad display to serve relevant advertisements in real-time, based on the inference from the audience looking at the ads. Advertising displays serve static or periodically shuffling ads, which change at regular intervals usually geared towards one segment of buyers. This results in a missed opportunity in terms of catering to other segments who would be near a billboard or a display. Learn how to build an intelligent solution where an advertising display uses an on-device camera, or feeds from nearby CCTV cameras of people passing by, to identify the audience and serving them personalized advertisements in near real time. We also cover how Amazon SageMaker and Amazon Rekognition can extract attributes like age, gender, height, face-positioning and use these attributes with Amazon Personalize to serve more relevant and targeted advertisements.

      Speaker:
      Vatsal Shah, Solutions Architect, AISPL


      Build a self-service know your customer application (Level 200)
      Join us in this demo to discover how simple it is to build a data-driven web application to automate manual and time-consuming processes. Learn how Know Your Customer (KYC) application conducts liveness detection check by requesting the user to perform random actions and validate these actions with Amazon Rekognition. We share how the user can upload key identification documents, leverage on Amazon Rekognition and Amazon Comprehend to analyze the content and gather name, date of birth and other key information. We also cover how this application can easily compare snapshots of the user's face and with the photo identification provided.

      Speaker: Arun Kumar Lokanatha, Startup Solutions Architect, AISPL

Waktu konferensi

Sesi Bahasa Indonesia
 GMT+7 (WIB)

Waktu: 12:30 - 17:30

English sessions
 GMT+7 (WIB)

Timing 1: 07:00 - 12:00
Timing 2: 12:30 - 17:30

Tingkat sesi yang dirancang untuk Anda

PERKENALAN
Level 100

Sesi difokuskan pada menyediakan ikhtisar layanan dan fitur AWS, dengan asumsi bahwa peserta belum terlalu memahami topik ini.

MENENGAH
Level 200

Sesi difokuskan pada menyediakan praktik terbaik, detail fitur layanan dan demo dengan asumsi bahwa peserta memiliki pengetahuan awal mengenai topik.

LANJUTAN
Level 300

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.  

PAKAR
Level 400

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

Craig Stires

Craig Stires,
Head of AI and Machine Learning Sales Lead, APJ, AWS

Olivier Klein

Olivier Klein,
Lead Technologist, APJ, AWS

.

Dean Samuels

Dean Samuels,
Lead Architect, ASEAN, AWS

.


Pelajari selengkapnya tentang machine learning di AWS

10,000

pelanggan memilih menggunakan AWS untuk machine learning

Terdepan dalam Gartner Magic Quadrant untuk layanan pengembang AI cloud

250+

fitur baru

10x

lebih produktif menggunakan Amazon SageMaker

89%

proyek deep learning di cloud berjalan di AWS


FAQ

1. Berapa biaya untuk menghadiri AWS Innovate?
2. Bagaimana cara mengakses acara online?
3. Siapa yang sebaiknya menghadiri AWS Innovate?
4. Apakah saya bisa mendapatkan konfirmasi untuk pendaftaran AWS Innovate saya?
5. Bagaimana saya dapat menghubungi penyelenggara konferensi online?

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

T: Bagaimana cara mengakses acara online?
J: Anda akan harus menetapkan nama pengguna dan sandi untuk menyelesaikan pendaftaran dan mengakses acara pada hari pelaksanaannya. Jika Anda memiliki pertanyaan, hubungi kami di aws-asean-marketing@amazon.com.

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

Mulai Menggunakan Amazon SageMaker

Mulai merancang dengan Amazon SageMaker di AWS Management Console.
Lihat Detail AWS Tingkat Gratis »

Craig Stires, Head of AI and Machine Learning Sales Lead, APJ, AWS

Craig Stires adalah Kepala AI dan Kepala Penjualan Machine Learning (Head of AI and Machine Learning Sales Lead) untuk Amazon Web Services, APJ. Beliau telah bekerja dengan beberapa organisasi yang paling inovatif di wilayah ini, saat organisasi tersebut mendesain AI dan machine learning, dan platform analitik dan menjadi didorong data. Ketika pertama kali pindah ke Asia, pada tahun 2001, beliau mendesain dan menerapkan solusi analitik untuk Keterlibatan Pelanggan, Manajemen Risiko, dan Analitik Operasional. Setelah beberapa tahun, beliau mendirikan perusahaan startup di Thailand yang membangun perangkat lunak kecerdasan prediktif. Setelah itu, dia membangun praktik riset Big Data untuk perusahaan analitik industri IDC. Setelah bertahun-tahun menasihati klien untuk membangun platform analitik yang dapat diskalakan, dioptimalkan, dan siap untuk bisnis, tiba saatnya untuk terjun langsung kembali. Pindah ke penyedia layanan cloud terbesar di dunia telah membuka pintu untuk bekerja bersama pelanggan membangun beberapa visi mereka yang paling menantang.

Olivier Klein, Lead Technologist, APJ, 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, 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.