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Submission: On February 01 via manual from IN — Scanned from DE
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Amazon InspectorAutomate vulnerability management
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AWS Key Management ServiceManaged creation and control of encryption keys
AWS Network FirewallNetwork security to protect your VPCs
AWS Resource Access ManagerSimple, secure service to share AWS resources
AWS Secrets ManagerRotate, manage, and retrieve secrets
AWS Security HubUnified security and compliance center
AWS ShieldDDoS protection
AWS Single Sign-OnCloud single sign-on (SSO) service
AWS WAFFilter malicious web traffic
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Learn about AWS Compliance offerings
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AWS Innovate - AI/ML Edition
Online conference Select region E-books & Case Studies AWS DeepRacer League
Resources Register now »
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Accelerate innovation and unlock new possibilities with AI & machine learning on
AWS
Register for online conference
Accelerate innovation and unlock new possibilities with AI & machine learning on
AWS
Register for online conference
50+
Sessions
Ask the
Experts
Live 1:1 Q&A
Certificate of
Attendance
Level up your skills
Customer
Stories
Use cases
Builders
Zone
Technical demos
50+
Sessions
Ask the
Experts
Live 1:1 Q&A
Certificate of
Attendance
Level up your skills
Builders
Zone
Technical demos
FEBRUARY 24, 2022
ASIA PACIFIC & JAPAN
OVERVIEW
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.
AGENDA
SESSIONS
CONFERENCE TIMINGS
FEATURED SPEAKERS
FAQS
HIGHLIGHTS
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!
Accelerate AI & ML innovations
Innovation stories
--------------------------------------------------------------------------------
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.
AI/ML use case solutions AI/ML use case solutions
Build, train & deploy ML models Build, train & deploy ML models
ML infrastructure ML infrastructure
Data infrastructure for ML workloads Data infrastructure for ML workloads
Scaling with ML Scaling with ML
Uplevel your ML skills Uplevel your ML skills
Builders Zone
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.
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.
Download Agenda at a Glance »
Register for online conference
--------------------------------------------------------------------------------
SESSIONS
* Opening keynote
* Rethink possible: Innovation stories
* Rethink possible: Accelerate AI & ML innovations
* AI/ML use case solutions
* Build, train & deploy ML models
* ML infrastructure
* Data infrastructure for ML workloads
* Scaling with ML
* Uplevel your ML skills
* Closing
* Builders Zone
* Korean
* Japanese
* Bahasa Indonesia
* Opening keynote
* Opening keynote
Innovate faster and build a smarter business with AI and ML on AWS (Level
100)
In 2022, organizations and businesses across all industries will have new
opportunities to personalize, scale, and invent more compelling customer
experiences. They can also build deeper relationships with their customers,
run more efficient operations, and pivot with the latest innovations. A major
enabler of these is the accessibility of AI/ML for all types of
organizations. In this session, uncover how we are helping organizations to
launch more quickly, lower the technical bar to get started with AI/ML, and
reduce the barrier to accelerate adoption of AI/ML.
Speakers:
Olivier Klein, Chief Technologist, APJ, AWS
Dean Samuels, Chief Technologist, APJ, ASEAN, AWS
Duration: 60mins
* Rethink possible: Innovation stories
* Rethink possible: Innovation stories
About the track
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: 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.
Hosts:
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.
Duration: 90mins
* Rethink possible: Accelerate AI & ML innovations
* Rethink possible: Accelerate AI & ML innovations
About the track
Learn how AI and ML services are applied to applications and used in
real-world use cases across industries and organizations.
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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
* AI/ML use case solutions
* AI/ML use case solutions
About the track
Discover how AI and ML services can easily integrate with applications to
address common use cases and solve business challenges.
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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
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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.
Speakers:
Arun Balaji, Prototyping Engineer, AISPL
Jeeri Deka, Associate Solutions Architect, AISPL
Duration: 30mins
* Build, train & deploy ML models
* Build, train & deploy ML models
About the track
Find out how to build, train, and deploy machine learning (ML) models for any
use case with fully managed infrastructure, tools, and workflows
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
Duration: 30mins
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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.
Speakers:
Gaurav Sahi, Senior Manager, ISV Solutions Architect, AISPL
Kamal Manchanda, Solutions Architect, AISPL
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
* ML infrastructure
* ML infrastructure
About the track
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.
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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
* Data infrastructure for ML workloads
* Data infrastructure for ML workloads
About the track
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.
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.
Speakers:
Abhishek Mishra, Senior Neptune Specialist Solutions Architect, AWS
Roneel Kumar, Senior Database Solutions Architect, AWS
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
* Scaling with ML
* Scaling with ML
About the track
Dive deep into core concepts to help you easily scale and secure your machine
learning workloads on AWS.
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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
* Uplevel your ML skills
* Uplevel your ML skills
About the track
At AWS, our goal is to put ML in the hands of every developer and data
scientist. Learn and experiment how to use ML and transform the way we live
our daily lives.
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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
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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
Duration: 30mins
* Closing
* Closing
Accelerate rapid innovation with AI and ML (Level 200)
Organizations and businesses of all sizes and governments new and old had to
change across all facets, and adopt new technologies to accelerate innovation
with the shift to the digital world. This session provides a recap of the
days' sessions and addresses some of the commonly asked questions related to
AI/ML with AWS. Additionally, learn how AWS is freeing businesses and
builders to solve real-world business problems in any industry and innovate
with confidence. Find out the latest announcements and developments in AWS
machine learning, demos of new technology, and insights from customers. To
conclude, the session focuses on how 2022 will be the launchpad for all kinds
of change to continue that acceleration.
Speakers:
Olivier Klein, Chief Technologist, APJ, AWS
Dean Samuels, Chief Technologist, APJ, ASEAN, AWS
Duration: 45mins
* Builders Zone
* Builders Zone
About the zone
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.
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
Speakers:
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
Speakers:
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
Speakers:
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
Speakers:
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
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Sessions are focused on providing an overview of AWS services and features, with
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topics.
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Sessions dive deeper into the selected topic. Presenters assume that the
audience has some familiarity with the topic, but may or may not have direct
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FEATURED AWS SPEAKERS
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Dean Samuels,
Chief Technologist, ASEAN, AWS
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FEATURED CUSTOMER SPEAKERS
Rob Smedley,
Director of Data System, F1
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Biomedical Engineer & Technology, Futurist
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Andrew Rush,
President and COO, Redwire
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Glenn Gore,
CEO, Affinidi
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Dr. Michelle Dickinson
Nanotechnologist and Materials Engineer
Christian Seifert
CEO, Deutsche Fußball Liga (DFL)
Dr. James Kuffner
Representative Director and CEO, Woven Planet Holdings
Tatiana Calderón
Alfa Romeo Racing ORLEN Test Driver and Team Ambassador, F1
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FREQUENTLY ASKED QUESTIONS
1. Where is AWS Innovate hosted?
2. Who should attend AWS Innovate?
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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: Are there sessions in other languages?
A: AWS Innovate is also available in Korean, Japanese, and Indonesian.
Q: Do I need to attend all the timings?
A: The sessions are repeated across the timings to accommodate attendees in
different time zones. You may select any timing that works best for you.
Q: What is the price of attending AWS Innovate?
A: AWS Innovate is a free online conference.
Q: Can I get a confirmation of my AWS Innovate registration?
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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.
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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.
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.
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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.
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.
ML infrastructure
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.