Welcome to AWS Innovate Online Conference - AI/ML Edition. Join us on February 24 for this special edition, get inspired and learn how you can use AI and machine learning to accelerate innovation, scale effortlessly, and unlock new possibilities for your organization.
For Business: Rethink Possible
When deployed with the right strategies, AI & ML can increase agility, streamline processes, boost revenue by creating new products and improving existing ones, and enable faster, better decision-making. Find out how organizations are using AI & ML to accelerate these business outcomes today!
For Builders: Create Tomorrow
Take your AI/ML skills to the next level today! Get hands-on and step-by-step architectural and deployment best practices to help you build better, innovate faster, and deploy at scale. Whether you are just getting started with AI/ML, an advanced user, or simply curious about AI/ML, we have a specific track for your level of experience and job role.
Agenda (Asia Pacific & Japan)
Organizations use AI and machine learning services to make accurate predictions, get deeper insights from data, reduce operational overhead, improve customer experiences, and create entirely new lines of business. Join us to get the latest technologies and innovations in AI & ML, learn how to apply AI/ML to your organization, and take your skills to the next level.
Rethink possible: Innovation stories
Rethink possible: Accelerate AI & ML innovations
AI/ML use case solutions
Build, train & deploy ML models
Data infrastructure for ML workloads
Scaling with ML
Uplevel your ML skills
Rethink possible: Innovation stories
Rethink possible: Innovation stories (Level 100)
Discover the technology innovation stories which has helped capture new opportunities, grow revenue, and solve the big problems faced today and in the future. Join this session and uncover how machine learning, supercomputing, AI, and robotics are powering manufacturing in space, enabling the growth of "super dust rice" to feed the world’s growing population, helping us build the first human base camp on the moon, creating a new era of FORMULA 1 racing, and fighting climate change.
Olivier Klein, Chief Technologist for APJ, AWS
Dr. Michelle Dickinson, Nanotechnologist and Materials Engineer
Customer speaker #1: Rob Smedley, Director of Data Systems, F1
Learn how F1 used computational fluid dynamics to build a new F1 car design that enables closer, more exciting, wheel-to-wheel racing, a process that would have taken more than 470 years on a standard laptop.
Customer speaker #2: Tatiana Calderón, Alfa Romeo Racing ORLEN Test Driver and Team Ambassador, F1
Hear the driver’s perspective on the new style of racing the 2022 F1 car will enable and what these changes mean for F1 drivers and fans in the future.
Customer speaker #3: Dr. Jordan Nguyen, Biomedical Engineer & Technology Futurist
Preventing age-related disease, increasing food production, and conserving wildlife remain some of the biggest challenges we face today. Learn how advances in genomics research, powered by cloud, AI, and supercomputing, help us grow “super dust rice” in desert conditions, protect the Amur tiger from extinction, and eradicate age-related disease.
Customer speaker #4: Dr. James Kuffner, Representative Director and CEO, Woven Planet Holdings
Toyota’s Woven City project is a purpose-built city of the future being built at the foot of Mount Fuji. Designed using digital twin technology, the city will weave together leading-edge mobility and communications technology with green spaces and sustainable infrastructure.
Customer speaker #5: Andrew Rush, President and COO, Redwire
Redwire uses 3D printing, robotics, artificial intelligence, and machine learning, to manufacture satellites, structures, and products in the unique zero-gravity environment of space. This vision is advancing space exploration and creating better products for life on Earth.
Customer speaker #6: Sally Fouts, Director of The Climate Pledge, Amazon
The Climate Pledge is a commitment to be net zero carbon by 2040, which is ten years ahead of the Paris Agreement. Learn how the pledge encourages companies to join Amazon in taking climate action and accelerating goals, plans, and programs to address the urgency of climate change.
Customer speaker #7: Christian Seifert, CEO, Deutsche Fußball Liga (DFL)
DFL CEO Christian Seifert shares how DFL has taken the live stadium experience of German Bundesliga football and given it to the world through leading-edge media technology.
Customer speaker #8: Glenn Gore, CEO, Affinidi
Affinidi uses Web 3.0 to power self-sovereign identity solutions, with personal data owned by the individual, creating new business models worldwide.
Speaker: Tom Soderstrom, Chief Technologist, Worldwide Public Sector, AWS
Advances in technology combined with lower costs are powering the growth of the space economy through innovative projects to protect earth from space, improve sustainability in space, and open a new era in space exploration.
Rethink possible: Accelerate AI & ML innovations
Rethink machine learning for regulated industries (Level 100)
Reproducibility, traceability, explainability… the Machine Learning lifecycle for regulated industries is no easy feat, and building a data science platform for a bank or the government that supports this lifecycle requires a deep set of capabilities and experience. To close this gap between the classic Machine Learning lifecycle and regulated industry requirements, AWS provides a set of services and solutions to create secure, governed, and compliant Machine Learning environment, that do not compromise on data science teams’ agility. In this session, we share the best practices implemented for customers in highly regulated industries and the programs, resources, and tools available to help you build a successful data science and machine learning platform on AWS.
Speaker: Natacha Fort, Senior Domain Solutions Architect - AI/ML, AWS
Rethink machine learning for supply chain (Level 100)
Supply chain operations are facing increasing challenges such as uncertainty, increased risk, changes to demand patterns, and disruptions. Legacy processes with manual decisions and structures are struggling to keep up. Forecasting demand has long been fundamental to managing variability, and has been one of the most successful applications of machine learning in the industry. However there is only so much forecasting can do, especially in uncertain times. In this session, get an introduction to how companies are starting to use data to predict the effect of their actions, pick the best actions to take, and assist their decision making. You can also get insights to an example of this in practice for routing in last-mile delivery.
Speaker: Eden Duthie, Senior Data Science Manager, AWS
Reinventing industrial operations with AI and ML (Level 100)
In this session, learn how industrial and manufacturing customers embed intelligence in their production processes to improve operational efficiency, quality control, security, and workplace safety. By combining sophisticated machine learning, sensor analysis, and computer vision capabilities, we can address common challenges faced by industrial customers, and represent the most comprehensive suite of cloud-to-edge industrial machine learning services available. Find out why customers of all sizes and across all industries are using AWS services to make machine learning core to their business strategy.
Speaker: Simon Johnston, Principal AI/ML GTM Specialist, AWS
AI and ML product development: How you can leverage AI and create productive teams to deliver transformative experiences (Level 100)
AI/ML techniques are increasingly important fundamentals to product delivery. In this session, we look at how companies are taking advantage of AI/ML capabilities to gain a competitive advantage through reduced operational costs, better user experiences, and more rapid innovation. We review the core components in the AWS Artificial Intelligence suite of services that allow you to build transformative products without requiring prior machine learning expertise. We also dive into how companies can create strong AI/ML product and engineering teams, aligned to shared goals, that deliver a roadmap of innovation and value. To conclude, we look at ways to conceptualize new product opportunities, based on both existing and emerging technology.
Speaker: Jonathan Hedley, Principal AI/ML Specialist Solutions Architect, AWS
AI/ML use case solutions
Modernize your CX innovation with ML-powered Amazon Connect (Level 200)
Customer experience remains one of the most important strategic measurements for organizational performance but keeping pace with customer behavior can be challenging. Amazon Connect is a simple to use, cloud-based contact centre service that makes it easy for any business to deliver engaging customer service interactions. Using the Amazon Connect integration with the machine learning services on AWS, you can use self-service configuration tools to accomplish in days what would often have taken months. In this session, learn how embedding AWS ML technologies into a cloud contact centre solution helps modernize and drive operational advances.
Speaker: Sumit Patel, Solutions Architect, AWS
Automate data extraction and analysis: Build an intelligent document processing and search solution at scale (Level 200)
Organizations hold large amount of business data in different formats and structures, used by various stakeholders. These organization-wide data vary largely on data origin, formats, language and content. Teams need to spend hours to understand all aspects of the data to derive information, which is not scalable, driving costs higher with mixed results. In this session, learn how Amazon Textract, Amazon Comprehend and Amazon Kendra can be used to extract data from multiple content repositories, uncover valuable insights, enable intelligent search, and discover meaningful insights with your data and at scale.
Speaker: Hariharan Suresh, Senior Solutions Architect, AWS
Fraud detection with Amazon Fraud Detector and Amazon SageMaker (Level 200)
Business fraud is a growing concern across online and offline transactions. Fraud and anomaly detection are one of the most important use cases for the Fintech startups. This session provides an overview on how you can use machine learning with Amazon SageMaker and Amazon Fraud Detector to implement customized fraud detection and prevention solutions. Learn how to proactively identify these use cases, and implement changes to protect your business and your customers.
Speaker: Rohini Gaonkar, Senior Developer Advocate, AISPL
Easily build custom computer vision models using Amazon Rekognition (Level 200)
Do you want to use computer vision in your projects, but find the idea of training a custom neural network model daunting? Have you used pre-trained computer vision models, but find that these models do not cover every aspect of your use case? With Amazon Rekognition Custom Labels, you can easily build computer vision models without needing an expert data scientist. In this session, learn how to prepare your dataset, customize Amazon Rekognition models with your data, and deploy these models in an application.
Speaker: Arun Kumar Lokanatha, Solutions Architect, AISPL
Using AI to automate video content moderation and compliance (Level 200)
The daily volume of User Generated Content (UGC) and third-party content has been increasing substantially across industries and platforms such as social media, social gaming, online forums, dating & matrimonial, including photo sharing websites. Customers often want to review audio, image, video, and text content to ensure that their end-users are not exposed to potentially inappropriate or offensive material, such as profanity, violence, drug use, adult products, nudity, or disturbing content. In addition, service providers may be required to ensure that the audio and video content they create or license are compliant with guidelines for various geographies or target audiences. In this session, learn how you can use Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend to streamline and automate your image and video moderation workflows using machine learning. We show you how you can use fully managed image, video, text, and speech moderation APIs & automated machine learning to proactively detect inappropriate, unwanted, or offensive content at scale and increase brand safety for you and your customers.
Speaker: Christer Whitehorn, Senior Elemental Specialist Solutions Architect, APJ, AWS
Using Hugging Face models on Amazon SageMaker (Level 200)
The field of natural language processing (NLP) is developing rapidly, and NLP models are growing increasingly large and complex. Through strong ecosystem partnerships with organizations like Hugging Face and advanced distributed training capabilities, Amazon SageMaker is one of the easiest platforms to quickly train NLP models. In this session, learn how to quickly train an NLP model from the Hugging Face transformers library with just a few lines of code using PyTorch or TensorFlow as well as SageMaker’s distributed training libraries.
Speaker: Praveen Jayakumar, Senior Manager, AIML Specialist Solutions Architect, AISPL
Running intelligent applications at edge using AWS AI/ML (Level 200)
Across a wide range of industries, there are a diverse set of use cases driving requirements for running intelligent applications closer to the consumers. These use cases range from personalized computing, real-time gaming, robotic manufacturing and assembly, predictive maintenance, video security, connected buildings and health & wellness services. We are seeing an explosion in the number and diversity of Edge computing hardware designed for such types of intelligent deep learning applications. In this session, learn how the power of AI/ML and IoT can be brought as close as possible to the challenging edge environments to provide data and create these intelligent applications.
Arun Balaji, Prototyping Engineer, AISPL
Jeeri Deka, Associate Solutions Architect, AISPL
Build, train & deploy ML models
Get started with Amazon SageMaker in minutes (Level 100)
Amazon SageMaker is a fully managed service that provides every developer, business analyst, and data scientist with the ability to prepare build, train, and deploy machine learning (ML) models quickly. 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 ML applications which helps more people innovate with ML. Through a choice of tools like integrated development environments for data scientists and developers, and no-code visual interfaces for business analysts. Learn how to prepare, build, train, tune, deploy, and manage your first machine learning model on AWS.
Speaker: Aashmeet Kalra, Principal Solutions Architect, AWS
Data preparation: Using Amazon SageMaker and AWS Glue DataBrew (Level 200)
Machine learning helps us find patterns in data—we then use the patterns to make predictions about new data points. Machine learning models are only as good as the data that is used to train them. After the data is collected, the integration, annotation, preparation, and processing of that data is critical. An essential characteristic of suitable training data is that it is provided in a way that is optimized for learning and generalization. In this session, we go over Amazon SageMaker Data Wrangler and AWS Glue DataBrew offerings and learn how to prepare your data for ML. We explain the process of cleaning and transforming raw data prior to processing and analysis. Data preparation should start with a small, statistically valid sample, and iteratively be improved with different data preparation strategies, while continuously maintaining data integrity. We show how Amazon SageMaker Suite provides multiple features which helps us construct the dataset and transform the data.
Gaurav Sahi, Senior Manager, ISV Solutions Architect, AISPL
Kamal Manchanda, Solutions Architect, AISPL
Build machine learning models with Amazon SageMaker optimal for your use case (Level 200)
Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models by bringing together a broad set of capabilities purpose-built for ML.. Based on your specific use case, in Amazon SageMaker you can pick from over 15 algorithms that are built-in and optimized for Amazon SageMaker or you can build models using popular deep learning or machine learning frameworks managed by AWS or bring your own container. You can also build models using over 150 pre-built models from popular model zoos available with just a few clicks. In this session, we provide an overview on the various ways you can build models with Amazon SageMaker efficiently, focusing on a range of SageMaker capabilities including; in-built algorithms, framework containers, Amazon SageMaker Autopilot and Amazon SageMaker JumpStart.
Speaker: Romina Sharifpour, AI/ML Specialist Solutions Architect, AWS
Train ML models quickly and cost-effectively with Amazon SageMaker (Level 300)
In this session, learn how to reduce time and cost to train and tune machine learning (ML) models without the need to manage infrastructure. We explain how Amazon SageMaker can easily train and tune ML models using built-in tools to manage and track training experiments, automatically choose optimal hyperparameters, debug training jobs, and monitor the utilization of system resources such as GPUs, CPUs, and network bandwidth. We go through how to add either data parallelism or model parallelism to your training script with a few lines of code, and the Amazon SageMaker distributed training libraries automatically split models and training datasets across GPU instances to help you complete distributed training faster.
Speaker: Alex Thewsey, AI/ML Specialist Solutions Architect, AWS
Bias detection and explainability in ML (Level 300)
Machine learning is increasingly used to assist decision making in financial services, education, transportation and healthcare. As decision support systems become more automated, there is a prevailing need to increase fairness-awareness and provide explanations for decisions made by machine learning models. In this session, we share how you can use Amazon SageMaker Clarify to identify different types of data and model bias, and understand how a prediction was generated through model explainability.
Speaker: Pauline Kelly, Solutions Architect, AWS
Optimizing Amazon SageMaker endpoints using serverless deployments and instance recommendations (Level 200)
Many customers have ML applications with intermittent or unpredictable traffic patterns. Selecting a compute instance with the best price performance for deploying machine learning (ML) models is a complicated, iterative process that can take weeks of experimentation. Rather than provisioning for peak capacity upfront, which can result in idle capacity or building complex workflows to shut down idle instances, you can now use Amazon SageMaker serverless inference and Amazon SageMaker Inference Recommender. In this session, learn to select serverless when deploying your ML model and how Amazon SageMaker automatically provisions, scales, and turns off compute capacity based on the volume of inference requests. Use Amazon SageMaker Inference Recommender to load test and automatically select the right compute instance type, instance count, container parameters, and model optimizations for inference to maximize performance and minimize cost. Dive deep into these new features, available in preview.
Speaker: Kapil Pendse, Principal Solutions Architect, AWS
Implementing MLOps with Amazon SageMaker (Level 300)
MLOps practices help data scientists and IT operations professionals collaborate and manage the production ML workflow, including data preparation and building, training, deploying, and monitoring models. This session explains some of the key MLOps features in Amazon SageMaker, with particular focus on how SageMaker Feature Store helps in integrating data and ML best practices to increase automation and improve the quality of end-to-end ML workflows.
Speaker: Alessandro Cerè, AI/ML Specialist Solutions Architect, AWS
Rapidly launch ML solutions at scale on AWS infrastructure (Level 200)
AWS offers the broadest and deepest services around quickly building and launching AI and machine learning for all types of organizations, businesses and industries. In this session, we explain the various options to rapidly deploy your inference models on AWS, managing training and inference workflows, and choosing the right instance. We also run a demo to demonstrate the simplicity and ease of use.
Speaker: Santhosh Urukonda, Prototpying Architect, AISPL
Build an end-to-end machine learning platform using Kubeflow on Amazon EKS (Level 300)
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. Kubeflow is dedicated to making ML deployments on Kubernetes simple, portable, and scalable. In this session, learn how you can leverage Kubeflow on Amazon EKS to deploy best-of-breed open source machine learning systems to provide data scientists with all the tools they need to run machine learning in the cloud. To conclude, we leverage Kubeflow on Amazon EKS and dive into notebooks, model training, AutoML, workflows, and model serving.
Speaker: Ben Friebe, ISV Solutions Architect, AWS
Machine learning inference with AWS Lambda and Amazon EFS (Level 200)
Machine learning is a complex task and it demands lots of resources to train and deploy ML model at scale for production. Training is only half the story; once you have trained your model, you typically want to use it to make predictions and there is a lot of focus on training. However running inference (prediction) in production represents the majority of the cost in ML workloads. In this session, we share how you can deploy an ML model using AWS Lambda and perform the inference via Amazon API Gateway in a cost effective and scalable manner. Learn how to build and deploy the whole application in an automated way, using AWS SAM (Serverless Application Model).
Speaker: Suman Debnath, Principal Developer Advocate, AISPL
Data infrastructure for ML workloads
Performing sentiment analysis on transactional and graph data with Amazon Aurora & Amazon Neptune ML integration (Level 200)
Machine learning algorithms have become one of the key competitive fronts between huge tech firms, with many sectors interested in employing them to boost efficiency and save costs. In recent years, a variety of services have emerged to aid in the construction, training, fine-tuning, and deployment of machine learning models for various businesses. AWS managed databases now offer in-built ML integration. In this session, we discuss the use of machine learning integration with Amazon Aurora and Neptune databases. Amazon Aurora machine learning enables you to add ML-based predictions to applications via the familiar SQL programming language and you do not need to learn separate tools or have prior machine learning experience. It provides simple, optimized, and secure integration between Aurora and AWS ML services without having to build custom integrations or move data around. This allows developers working with the Postgres or MySQL engines to add capabilities to their application using familiar SQL techniques, syntax, and interfaces. The session also shares how to utilize transactional data in Amazon Aurora to add machine learning-based predictions to apps and use familiar SQL programming language to get deep insights into all aspects of the data. Learn how to leverage Amazon Neptune ML to create classification, regression, and link prediction ML models on graph inside Neptune. Amazon Neptune ML is a new capability of Neptune that uses Graph Neural Networks (GNNs), a machine learning technique purpose-built for graphs, to make easy, fast, and more accurate predictions using graph data. To conclude, find out how Amazon Neptune ML, can improve the accuracy of most predictions for graphs by over 50% when compared to making predictions using non-graph methods.
Abhishek Mishra, Senior Neptune Specialist Solutions Architect, AWS
Roneel Kumar, Senior Database Solutions Architect, AWS
Integrate easy-to-use machine learning into your analytics workloads (Level 200)
Machine learning can help your organization imagine new products or services, transform customer experiences, streamline business operations, improve decision-making, and much more. However, organizations find it difficult to scale such initiatives due to limited market of skilled professionals. At AWS, we are building a new wave of data and analytics tools enabling the development of sophisticated insights with little upskilling required. In this session, learn the easy-to-use machine learning features and capabilities integrated with AWS Analytics services such as AWS Glue, Amazon Kinesis Data Analytics, Amazon OpenSearch, Amazon Athena, Amazon Redshift and Amazon QuickSight which helps scaling ML at every stage of your analytics pipeline, beyond the role of data scientists to a more varied set of personas such as data engineers, database developers, data analysts, BI professionals, and the line of business.
Speaker: Niladri Bhattachrya, Senior Analytics Specialist Solutions Architect, AWS
Democratize machine learning with Amazon Redshift ML to drive employee attraction, not attrition (Level 300)
Amazon Redshift ML, powered by Amazon SageMaker Autopilot, makes it easy for data analysts, data scientists, BI developers to create, train, and apply ML models using familiar SQL commands in Amazon Redshift data warehouses. Amazon Redshift ML also provides the ability to Bring Your Own Model (BYOM), prebuilt using Amazon SageMaker for local inferences capabilities directly in Amazon Redshift at no additional cost. In this session, learn how to simplify machine learning with Amazon Redshift ML to predict employee turnover propensity based on data from employee survey, as a SQL function in queries and reports. With this data driven approach, organization can proactively engage employees at risk of leaving to protect intellectual capital.
Speaker: Mary Law, Senior Manager Analytics, APJ Acceleration Lab, AWS
Accelerate your SageMaker model training with Amazon FSx for Lustre and Amazon S3 (Level 200)
Organizations have accumulated massive amounts of data, and are continuing to accumulate data. This stored data allow them to generate different types of insights including reporting on historical data, and deploy machine learning. Building effective machine learning models requires storage that can scale in capacity and performance to handle workload demands with high throughput and low-latency file operations. In this session, we show how you can speed up machine learning training jobs by seamlessly leveraging Amazon FSx for Lustre and Amazon S3 for more informed decision making and your user experiences.
Speaker: Gaurav Singh, Solutions Architect, AISPL
Scaling with ML
Setting up secure, well-governed machine learning environments on AWS (Level 200)
Whether your organization is starting on its AI/ML journey, or has a large number of projects in production, it is vital to implement verifiably secure environments to protect data. In this session, we share how you can organize, standardize, and expedite the provisioning of governed ML environments using recommended AWS security best practices.
Speaker: Michael Stringer, Senior Domain Solutions Architect - Security, AWS
Run common ML use cases without writing a single line of code using Amazon SageMaker Canvas (Level 200)
As an organization facing business problems and dealing with data on a daily basis, the ability to build systems that can predict business outcomes becomes very important. This ability lets you solve problems and move faster by automating slow processes and embedding intelligence in your IT systems. As a business user or data analyst, you’d like to build and use prediction systems based on the data that you analyze and process every day, without having to learn about hundreds of algorithms, training parameters, evaluation metrics, and deployment best practices. In this session, learn how to use Amazon SageMaker Canvas to run some common ML use cases like classification and churn prediction; use a visual interface without writing a single piece of code or have any ML expertise.
Speaker: Vatsal Shah, Senior Solutions Architect, AISPL
AI and machine learning powered by Intel (Level 100)
In this session, learn how Amazon EC2 instances with Intel® performance optimizations enable customers to create intelligent and innovative new products and experiences for improved decision-making and customer engagements.
Speaker: Akanksha Balani, AWS APJ Alliance head, Intel
Uplevel your ML skills
Getting started to learn and experiment ML with Amazon SageMaker Studio Labs (Level 100)
Amazon SageMaker Studio Lab offers an open-source Jupyter notebook environment integrated with the GitHub software development platform and preconfigured with the most popular ML tools, frameworks, and libraries so that you can write ML code immediately without having to configure the ML environment. Using Amazon SageMaker Studio Lab, you can work on ML projects without worrying about saving models. It’s as easy as closing your laptop and coming back later. In this session, learn how Amazon SageMaker Studio Lab is accelerating your journey on machine learning with a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security—all at no cost—for anyone to learn and experiment with ML. All you need to get started is a valid email address—you do not need to configure infrastructure or manage identity and access or even sign up for an AWS account.
Speaker: Donnie Prakoso, Senior Developer Advocate, AWS
Applying AWS machine learning to next-gen DevOps (Level 300)
While DevOps technology has evolved dramatically over the last few years, it is still challenging. Issues related to concurrency, security, or handling of sensitive information require expert evaluation and often slip through existing mechanisms like peer code reviews and unit testing. Even for organizations that can invest in developers who are expert code reviewers, the pace at which the software is developed creates high volumes of complex code that are difficult to review manually. In this session, learn how the AWS next-gen DevOps portfolio helps augment your developer’s expertise with machine learning (ML) capabilities to establish automation and more proactive mechanisms that enable teams to innovate faster with confidence.
Speaker: Michele Ricciardi, Senior Specialist Solutions Architect, DevAx, AWS
Machine learning for developers: Enhance your customer experience (Level 200)
Amazon has been applying machine learning to create artificial intelligence features within its products and services for over 20 years. In this session learn how AWS machine learning and AI services enables you to add intelligence into your applications. We showcase how you can enhance user experience by integrating Amazon Personalize with your web applications using microservices.
Speaker: Janos Schwellach, Senior Specialist Solutions Architect, DevAx, AWS
Start your engines with AWS DeepRacer (Level 200)
Looking for an interesting and fun way to learn about Reinforcement Learning (RL), then look no further than AWS DeepRacer, where you can learn to build ML models quickly. You can then experiment with different algorithms, neural network configurations and simulate it on a virtual racetrack. Once you have built your ML model, you can race in the AWS DeepRacer League; the world’s first global autonomous racing league, open to anyone to compete for prizes and glory. Developers! start your engines today.
Speaker: Calvin Ngo, Developer Specialist Solutions Architect, AWS
Process unstructured documents (Level 200)
Extracting insights from unstructured documents manually is a tedious process and requires lot of time and effort. Learn how to add intelligence to process unstructured documents and extract meaningful information using AWS AI services such as Amazon Textract and Amazon Comprehend. In this demo, we showcase how easy it is to process unstructured content like product reviews and extract specific details related to the customer requirements, including overall sentiment of the reviews using Amazon Textract and Amazon Comprehend. To conclude, we also review the pipeline created using AWS Step Functions and AWS AI services in this demo.
AWS services: Amazon Textract, Amazon Comprehend, AWS Step Functions, AWS Lambda, AWS SNS and Amazon DynamoDB
Speaker: Santhosh Urukonda, Prototyping Architect, AISPL
Smart interview scheduling automation (Level 200)
Often enterprises find it hard to shortlist right candidates for tech roles as it is hard to bring in consistency & efficiency in the way interviews happen. It takes lot of manual effort to interview a candidate and assess his capabilities, and equally challenging to find interviewer’s time to schedule and materialize the interviews. Given the COVID situation, it has become more difficult to conduct these interviews in-person and an ML solution that can intelligently automate interview scheduling will definitely help lots of enterprises improve their hiring process. In this demo, we showcase a front-end web portal to mimic the interview experience where a user is presented with a tech question, and the user can provide answer verbally or submit a written answer. The ML model then evaluates and assesses the answers for its correctness, and quickly revert with a score within minutes.
AWS services: Amazon SageMaker, Amazon SageMaker Ground Truth, Amazon Polly, Amazon Transcribe, AWS Step Functions, AWS Lambda, Amazon SQS, Amazon DynamoDB, AWS Amplify
Arun Balaji, Prototyping Engineer, AISPL
Satheesh Kumar, Enterprise Architect, AISPL
Build an organization’s expenditure forecast (Level 200)
Today businesses from fast paced startups to large enterprises and traditional businesses generate huge amount of sequential data points in unit of time. Organizations need mechanism to predict patterns and future time series data, looking at historical data to other variables. Machine learning can be used to forecast any time series data and serve use cases such as retail demand, manufacturing demand, travel demand, revenue & budget planning, IT capacity, logistics, price prediction, web traffic and more. In this session, we look at how developers can leverage and build an organization’s expenditure forecast solution with the help of Amazon Forecast and other AWS technologies. Learn how developers with no prior ML experience can build sophisticated forecasting model that uses machine learning to combine time series data and additional data variables.
AWS services: Amazon Forecast
Speaker: Darshit Vora, Startup Solutions Architect, AISPL
Reinventing a metro transport crowd management system (Level 300)
Metro train ridership has grown significantly over the past decades with exponential growth expected in the near future as commuters increased. Crowding at metro stations are experienced frequently, resulting in safety concerns, decreased comfort levels and increased in wait and travel time. Despite numerous efforts in implementing an effective crowd management program, the program still falls short in containing the formation of crowds causing long queue lines, affecting commuter’s wait time at the platform and delay in boarding the train. In this session, we show how you can create and deploy a Crowd Density Estimation ML Solution leveraging AWS services like Amazon Rekognition (deep learning-based image and video analysis service), AWS Lambda, Amazon API Gateway, Amazon SNS to help avoid crowding at the stations and platforms, and provide a better daily commuting experience with trains and coaches.
AWS services: Amazon Rekognition (deep learning-based image and video analysis service), AWS Lambda, Amazon API Gateway, Amazon SNS
Speaker: Kayalvizhi Kandasamy, Senior Solutions Architect, AISPL
Plant leaf disease detection with Amazon Rekognition Custom Labels (Level 200)
Farming can be affected by many diseases, leading to significant economic losses due to reduction of yield and loss of crops quality. The health condition of a crop plant can often be assessed by the condition of its leaves. Identifying symptoms early and controlling diseases before they spread too far is pivotal to farmers. However, manually identifying the leaf that is infected, the type of infection, and the required disease control solution is hard, costly, and error-prone. This is where an automated machine learning (ML) solution with computer vision (CV) can help. In this session, we showcase how you can build an end-to-end disease detection, identification, with an intelligent recommendation engine using Amazon Rekognition Custom Labels can help solve this farming challenge.
AWS services: Amazon Rekognition Custom Labels
Speaker: Dhiraj Thakur, Senior Partner Solutions Architect, GSI, AISPL
Automating claims adjudication workflow using Amazon Comprehend Medical and Amazon Textract (Level 200)
When a medical claim is submitted, the insurance provider must process the claim to determine the correct financial responsibility of the insurance provider and the patient. The process to determine this is broadly known as claims adjudication. It involves creating a claims processing workflow that checks each claim for authenticity, correctness, and validity based on coverage. Some of the steps in this workflow involve working with unstructured data which requires manual steps in the workflow to extract the information buried in the unstructured notes. To process volumes of claims in a cost effective and scalable manner, healthcare payers are increasingly looking at machine learning to reduce dependency on humans and rely on automation as much as possible. Additionally, analyzing and interpreting health claim data is powerful in driving improvements in population health to address issues related to cost, quality and outcomes. According to the CDC report analyzing claim documents will help identify certain behaviors would help in preventing or delaying the development of a medical condition. AWS provides a comprehensive list of machine learning and analytics services that allow developers, irrespective of their background, to start integrating machine learning and analytics technology into their applications. Through this session, we demonstrate how we can use two AWS AI services, Amazon Textract and Amazon Comprehend Medical to automate the claims adjudication workflow and run analytics on top to extract entities using Amazon Athena.
AWS services: Amazon Textract, Amazon Comprehend Medical and Amazon Athena
Speaker: Joinal Ahmed, Associate Solutions Architect, AISPL
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 OpenSearch Service, and AWS IoT to run this model at the edge with TensorFlow and an NVIDIA Jetson AGX Xavier Developer Kit.
AWS services: Amazon SageMaker, Amazon OpenSearch Service, AWS IoT
Kapil Pendse, Principal Solutions Architect, AWS
Janos Schwellach, Senior Specialist Solutions Architect, DevAx, AWS
Multilingual omnichannel contact center (Level 200)
Language barriers between a customer and agent can be a challenge for any contact centre. In this session, we show how to automatically translate chat conversations between users in real-time, in the preferred language across different channels, allowing for a better customer experience.
AWS services: Amazon Comprehend, Amazon Transcribe, Amazon Translate
Gaurav Sahi, Senior Manager, ISV, Solutions Architect, AISPL
Jackysh Bangera, Solutions Architect, AISPL
Workplace safety with Amazon Rekognition (Level 300)
Today, businesses are looking for ways to adapt to the challenges created by COVID-19. For example, retailers need to keep their employees and customers safe as they interact in physical proximity. Join this session to learn how you can use Amazon Rekognition, a fully managed computer vision service to build automated and scalable workplace safety compliance solutions.
AWS services: Amazon Rekognition
Speaker: Kashif Imran, Software Development Manager, AI, Computer Vision, AWS
Improving call center efficiency and omnichannel customer experience with QnA bot (Level 200)
Find out how to build an interactive and smart QnA bot. The AWS QnABot is an open source, multi-channel, multi-language conversational chatbot built on Amazon Lex that responds to your customer’s questions, answers, and feedback. Without programming, the AWS QnABot solution allows customers to quickly deploy self-service conversational AI on multiple channels including their contact centers, web sites, social media channels, SMS text messaging, or Amazon Alexa.
AWS services: Amazon Lex, Amazon Kendra, Amazon Comprehend, Amazon Polly, Amazon Translate, Amazon Connect
Nieves Garcia, Tech BD AI/ML, AWS
Melwin Pais, Solutions Architect, AWS
Build an AWS Deep Lego Train (Level 300)
Learn how to build a practical solution that works to solve a real world public safety challenge at railway level crossings. In this session, we explain how to architect AI/ML solutions that make real time decisions out in the field, and demonstrate just how easy it can be to get started with computer vision leveraging AWS Services. Find out how the solution only uses everyday items such as Lego, used laptops, and consumer grade web cameras to demonstrate that AI/ML is a highly accessible technology, which does not require significant investment to achieve meaningful outcomes.
AWS Services: Amazon SageMaker, Amazon SageMaker Ground Truth, AWS IoT Core, AWS IoT Greengrass
Speaker: Xavier Hutchinson, Regional Solutions Architect, ANZ, AWS
Detect online fraud faster and protect your business with Amazon Fraud Detector (Level 200)
Online fraud is estimated to be costing businesses more than $4.2 Billion a year, according to the FBI’s Internet Crime Report 2020. In this session, you will learn how with Amazon's 20 years of experience fighting fraud translates into an AI service that can help you detect more online fraud faster. Learn how Amazon Fraud Detector transforms raw data into highly accurate ML-based fraud detection models, does data preparation and validation, feature engineering, data enrichment and model training and tuning, all at the click of a few buttons with no prior ML experience required.
AWS Service: Amazon Fraud Detector
Speaker: Neil DCruz, Startup Solutions Architect, AISPL
Secure & compliant implementation of AI services - Amazon Transcribe (Level 200)
Customers uses Amazon Transcribe to convert speech to text and to create applications that incorporate the content of audio files. Whether your organization is starting on its AI/ML journey, or has a large number of projects in production, it is vital to implement standardized, secure, and compliant AI/ML environments using recommended AWS security best practices. In this demo, learn how Amazon Transcribe provides an accurate voice-to-text technology. We also demonstrate how to use other AWS services to setup auditable, secure, and compliant Amazon Transcribe resources.
AWS Services: Amazon S3, Amazon Transcribe, Amazon Key Management Service
Speaker: Utkarsh Pandey, Startup Solutions Architect, AISPL
Build an intelligent marketing kiosk using AWS AI/ML services (Level 200)
In this session, learn how to build a Smart Ad Display capable of serving relevant advertisements in real-time, based on the inference from the audience looking at it. Most advertising displays in retail today serve static or periodically shuffling ads, which change at regular intervals and are usually geared towards one segment of buyers. This leads to retailers to missing the opportunity of catering to other segments who would be near a billboard or a display. This session demonstrates how to build an intelligent solution where an advertising display can use an on-device camera, or feeds from nearby CCTV cameras of people passing by to identify the audience and serve them a personalized advertisement in near real-time. Learn how to use services like Amazon SageMaker and Amazon Rekognition to extract attributes like age, gender, height, and face-positioning and then feed these attributes into Amazon Personalize to serve more relevant, targeted advertisements.
AWS Services: Amazon Personalize, Amazon Rekognition, Amazon SageMaker, Amazon EventBridge, AWS Lambda, AWS AppSync, Amazon Kinesis, Amazon DynamoDB, Amazon Simple Storage Service (Amazon S3)
Speaker: Vatsal Shah, Senior Solutions Architect, AISPL
Frequently Asked Questions
1. Where is AWS Innovate hosted?
2. Who should attend AWS Innovate?
3. How do I get the certificate of attendance?
4. Are there sessions in other languages?
5. Do I need to attend all the timings?
6. What is the price of attending AWS Innovate?
7. Can I get a confirmation of my AWS Innovate registration?
8. How can I contact the online conference organizers?
Q: Where is AWS Innovate hosted?
A: AWS Innovate is an online conference. After filling up the registration form, you will receive an email to complete your registration. Please follow the instructions and complete the steps to receive the confirmation email and gain access to the event on February 24, 2022.
Q: Who should attend AWS Innovate?
A: Whether you are new to AWS or an experienced user, you can learn something new at AWS Innovate. AWS Innovate is designed to help you develop the right skills to innovate faster, enable new efficiencies, and make quicker, accurate decisions.
Q: How do I get the certificate of attendance?
A: Complete watching 5 or more sessions on the live day to receive a certificate of attendance by March 11, 2022. This will be sent to your email address that you used to register for the event.
Q: Can I get a confirmation of my AWS Innovate registration?
A: After filling up the registration form, you will receive an email to complete your registration. Please follow the instructions and complete the steps to receive the confirmation email and gain access to the event on February 24, 2022.
Q: How can I contact the online conference organizers?
A: If you have questions that have not been answered in the FAQs above, please email us.
Craig Stires, Head of AI and Machine Learning, APJ, AWS
Craig Stires is the Head of AI and Machine Learning Sales Lead for Amazon Web Services, APJ. He has worked with some of the most innovative organizations across the region, as they architect AI and machine learning, and analytics platforms and become data-driven. When he first moved to Asia, in 2001, he was designing and implementing analytics solutions for Customer Engagement, Risk Management, and Operational Analytics. After several years, he founded a Startup in Thailand building predictive intelligence software. Following that, built the Big Data research practice for industry analytics firm IDC. After years of advising clients to build scalable, optimized, and business-ready analytics platforms, the time was right to get hands-on again. Moving to the world's largest cloud services provider has opened the door to work together with customers to build some of their most exciting visions.
Olivier Klein, Chief Technologist, APJ, AWS
Olivier is a hands-on technologist with more than 10 years of experience in the industry and has been helping customers build resilient, scalable, secure, and cost-effective applications and create innovative and data-driven business models. He advises on how emerging technologies in the AI, ML, and IoT spaces can help create new products, make existing processes more efficient, provide overall business insights, and leverage new engagement channels for consumers.
Dean Samuels, Chief Technologist, ASEAN, AWS
Dean comes from an IT infrastructure background and has extensive experience in infrastructure virtualization and automation. He has been with AWS for the past five years and has had the opportunity to work with businesses of all sizes and industries. Dean is committed to helping customers design, implement, and optimize their application environments for the public cloud to allow them to become more innovative, agile, and secure.
Rethink possible: Innovation stories
Hear from leaders across the globe on how they are using machine learning, supercomputing, AI, and robotics to innovate at speed, accelerate business growth, and ‘Rethink possible’.
Rethink possible: Accelerate AI & ML innovations
Learn how AI & ML services are applied to applications and used in real-world use cases across industries and organizations.
AI/ML use case solutions
Discover how AI and ML services can easily integrate with applications to address common use cases and solve business challenges.
Build, train & deploy ML models
Find out how to build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.
Learn how to leverage AWS high performance, cost-effective, scalable infrastructure and easily customize the infrastructure to fit your performance and budget requirements for your ML workloads.
Data infrastructure for ML workloads
Data drives today’s businesses and economies. Learn how to build a solid data infrastructure to help you deliver high performance AI and ML models trained by data. Harness the power of data to unlock insights and create new possibilities today.
Scaling with ML
Dive deep into core concepts to help you easily scale and secure your machine learning workloads on AWS.