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100,000+ databases migrated to AWS 10,000+ data lakes and data warehouses deployed on AWS Hundreds of thousands of customers rely on AWS Databases
Build an End-To-End Pipeline With BERT, Tensorflow, and Amazon SageMaker
April 15th, 2021 | 9:00 AM - 1:00 PM PT
In this hands-on workshop, we will build an end-to-end AI/ML pipeline for natural language processing with Amazon SageMaker.
Attendees will learn how to:
- Ingest data into S3 using Amazon Athena and the Parquet data format
- Visualize data with pandas, matplotlib on SageMaker notebooks
- Run data bias analysis with SageMaker Clarify
- Perform feature engineering on a raw dataset using Scikit-Learn and SageMaker Processing Jobs
- Store and share features using SageMaker Feature Store
- Train and evaluate a custom BERT model using TensorFlow, Keras, and SageMaker Training Jobs
- Evaluate the model using SageMaker Processing Jobs
- Track model artifacts using Amazon SageMaker ML Lineage Tracking
- Run model bias and explainability analysis with SageMaker Clarify
- Register and version models using SageMaker Model Registry
- Deploy a model to a REST Inference Endpoint using SageMaker Endpoints
- Automate ML workflow steps by building end-to-end model pipelines using SageMaker Pipelines
Event Agenda
Welcome, Introductions, and Demo Setup
9:00 AM - 9:30 AM PT
- Workshop introduction and setup.
Ingest and Explore Data
9:30 AM - 10:00 AM PT
- Register and explore our dataset using AWS Glue, Amazon Athena and Parquet data.
Analyze Data for Bias
10:00 AM - 10:30 AM PT
- Perform data bias analysis using SageMaker Clarify.
BERT Feature Engineering
10:30 AM - 11:00 AM PT
- Understand BERT embeddings, and how to convert raw text into BERT features using Hugging Face and TensorFlow. Store and share features using SageMaker feature store.
Break
11:00 AM - 11:15 AM PT
BERT Fine-Tuning
11:15 AM - 11:45 AM PT
- Understand BERT pre-training vs. fine-tuning, and how to fine-tune a BERT model and create a text classifier using Hugging Face and TensorFlow.
Build an End-to-End BERT Text Classifier Pipeline
11:45 AM - 12:15 PM PT
- Build an end-to-end BERT text classifier pipeline.
Analyze Model for Bias and Explainability
12:15 PM - 12:45 PM PT
- Perform model bias analysis and explainability using SageMaker Clarify.
Register and Deploy Model
12:45 PM - 1:00 PM PT
- Register and approve model using SageMaker Model Registry, deploy model on REST inference endpoint.
Session Details
- Track1 Session 1
- Track2 Session 1
- Track3 Session 1
- Track4 Session 1
- Track5 Session 1
- Track6 Session 1
- Track7 Session 1
- Track8 Session 1
- Track9 Session 1
- Track10 Session 1
Track1 Session 1
Migrate your Oracle and SQL Server databases to
Organizations today are looking to free themselves from the constraints of on-premises databases and leverage the power of fully managed databases in the cloud. Amazon RDS is a fully managed relational database service that you can use to run your choice of database engines including open source engines, Oracle, and SQL Server in the cloud. Amazon RDS automates time-consuming database administration tasks and adds capabilities such as replication and Multi-AZ failover to make your database deployments more scalable, available, reliable, manageable, and cost-effective. This session covers why you should consider moving your on-premises Oracle & SQL Server deployments to Amazon RDS and the tools to get started.
Track2 Session 1
Migrate your Oracle and SQL Server databases to
Organizations today are looking to free themselves from the constraints of on-premises databases and leverage the power of fully managed databases in the cloud. Amazon RDS is a fully managed relational database service that you can use to run your choice of database engines including open source engines, Oracle, and SQL Server in the cloud. Amazon RDS automates time-consuming database administration tasks and adds capabilities such as replication and Multi-AZ failover to make your database deployments more scalable, available, reliable, manageable, and cost-effective. This session covers why you should consider moving your on-premises Oracle & SQL Server deployments to Amazon RDS and the tools to get started.
Track3 Session 1
Tab 3 content
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Track4 Session 1
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Track5 Session 1
Tab 5 content
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Track6 Session 1
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Track7 Session 1
Tab 7 content
Organizations today are looking to free themselves from the constraints of on-premises databases and leverage the power of fully managed databases in the cloud. Amazon RDS is a fully managed relational database service that you can use to run your choice of database engines including open source engines, Oracle, and SQL Server in the cloud. Amazon RDS automates time-consuming database administration tasks and adds capabilities such as replication and Multi-AZ failover to make your database deployments more scalable, available, reliable, manageable, and cost-effective. This session covers why you should consider moving your on-premises Oracle & SQL Server deployments to Amazon RDS and the tools to get started.
Track8 Session 1
Tab 8 content
Organizations today are looking to free themselves from the constraints of on-premises databases and leverage the power of fully managed databases in the cloud. Amazon RDS is a fully managed relational database service that you can use to run your choice of database engines including open source engines, Oracle, and SQL Server in the cloud. Amazon RDS automates time-consuming database administration tasks and adds capabilities such as replication and Multi-AZ failover to make your database deployments more scalable, available, reliable, manageable, and cost-effective. This session covers why you should consider moving your on-premises Oracle & SQL Server deployments to Amazon RDS and the tools to get started.
Track9 Session 1
Tab 9 content
Organizations today are looking to free themselves from the constraints of on-premises databases and leverage the power of fully managed databases in the cloud. Amazon RDS is a fully managed relational database service that you can use to run your choice of database engines including open source engines, Oracle, and SQL Server in the cloud. Amazon RDS automates time-consuming database administration tasks and adds capabilities such as replication and Multi-AZ failover to make your database deployments more scalable, available, reliable, manageable, and cost-effective. This session covers why you should consider moving your on-premises Oracle & SQL Server deployments to Amazon RDS and the tools to get started.
Track10 Session 1
Tab 10 content
Organizations today are looking to free themselves from the constraints of on-premises databases and leverage the power of fully managed databases in the cloud. Amazon RDS is a fully managed relational database service that you can use to run your choice of database engines including open source engines, Oracle, and SQL Server in the cloud. Amazon RDS automates time-consuming database administration tasks and adds capabilities such as replication and Multi-AZ failover to make your database deployments more scalable, available, reliable, manageable, and cost-effective. This session covers why you should consider moving your on-premises Oracle & SQL Server deployments to Amazon RDS and the tools to get started.
Multiple City Registration
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Sessions will focus on providing an overview of AWS services and features, with the assumption that attendees are new to the topic Sessions will focus on providing an overview of AWS services and features, with the assumption that attendees are new to the topic Sessions will focus on providing an overview of AWS services and features, with the assumption that attendees are new to the topic Sessions will focus on providing an overview of AWS services and features, with the assumption that attendees are new to the topic
Session Proficiency Levels Explained
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Level 100
Introductory
Sessions will focus on providing an overview of AWS services and features, with the assumption that attendees are new to the topic
-
Level 200
Intermediate
Sessions will focus on providing best practices, details of service features and demos with the assumption that attendees have introductory knowledge of the topics
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Level 300
Advanced
Sessions will dive deeper into the selected topic. Presenters assume that the audience has some familiarity with the topic, but may or may not have direct experience implementing a similar solution
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Level 400
Expert
Sessions are for attendees who are deeply familiar with the topic, have implemented a solution on their own already, and are comfortable with how the technology works across multiple services, architectures, and implementations
Why should you attend?
This event provides attendees with the chance to deeply explore some AWS AI/ML service offerings through in-depth demos and advanced technically focused sessions.
Who should attend Dev Days?
Anyone who would like to learn from a technical expert deep dive in AWS Services. Technical individuals that manipulate code, data, configuration/operations or design app architectures to build differentiated and successful applications in the Cloud, such as developers, data engineers, data scientists, enterprise application architects, IT admins/operators, infosec, or those experimenting with emerging tech.
Featured Speakers
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Chris Fregly,
Principal Developer Advocate, AI and Machine Learning, AWSChris is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is co-author of the O'Reilly Book, "Data Science on AWS". Chris is also the founder of many AI-focused global meetups including the global "Data Science on AWS" Meetup. He regularly speaks at AI and Machine Learning conferences across the world including O’Reilly AI, Open Data Science Conference (ODSC), and Nvidia GPU Technology Conference (GTC).
Previously, Chris was Founder at PipelineAI where he worked with many AI-first startups and enterprises to continuously deploy ML/AI Pipelines using Spark ML, Kubernetes, TensorFlow, Kubeflow, Amazon EKS, and Amazon SageMaker.
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Antje Barth,
Senior Developer Advocate, AI and Machine Learning, AWSAntje is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in Düsseldorf, Germany. She is co-author of the O'Reilly Book, "Data Science on AWS".
Antje is also co-founder of the Düsseldorf chapter of Women in Big Data. She frequently speaks at AI and Machine Learning conferences and meetups around the world, including the O’Reilly AI and Strata conferences. Besides ML/AI, Antje is passionate about helping developers leverage Big Data, container and Kubernetes platforms in the context of AI and Machine Learning.
Previously, Antje worked in technical evangelism and solutions engineering at MapR and Cisco where she worked with many companies to build and deploy cloud-based AI solutions using AWS and Kubernetes.
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Richard Boyd, Cloud Data Engineer, iRobot
Richard Boyd is a cloud data engineer with the iRobot Corporation’s Cloud Data Platform where he builds tools and services to support the world’s most beloved vacuum cleaner. Before joining iRobot, Richard built discrete event simulators for Amazon’s automated fulfillment centers in Amazon Robotics. His previous roles include cyber warfare systems analyst at MIT and research for the Center for Army Analysis. He holds advanced degrees in Applied Mathematics & Statistics.
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Raju Gulabani, VP of Databases, Analytics & AI, AWS
Raju Gulabani is VP of Databases, Analytics & AI within AWS at Amazon.com. He is responsible for P&<, product management, engineering and operations for Database services such as Amazon Aurora and Amazon DynamoDB, and Analytics services such as Amazon Redshift and Amazon EMR, as well as AI services like Amazon Lex, Amazon Polly, and Amazon Rekognition. Prior to joining Amazon in his current position in 2010, Raju spent four years at Google and built the Google Apps business (now known as G Suite).Earlier in his career, Raju founded an Intel backed Wi-Fi Voice over IP company as well as held engineering management positions at Microsoft.
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Ryan Kelly, Data Architect, Equinox
Ryan Kelly is a data architect at Equinox, where he helps outline and implement frameworks for data initiatives. He also leads clickstream tracking which helps aid teams with insights on their digital initiatives. Ryan loves making it easier for people to reach and ingest their data for the purposes of business intelligence, analytics, and product/service enrichment. He also loves exploring and vetting new technologies to see how they can enhance what they do at Equinox
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Richard Boyd, Cloud Data Engineer, iRobot
Richard Boyd is a cloud data engineer with the iRobot Corporation’s Cloud Data Platform where he builds tools and services to support the world’s most beloved vacuum cleaner. Before joining iRobot, Richard built discrete event simulators for Amazon’s automated fulfillment centers in Amazon Robotics. His previous roles include cyber warfare systems analyst at MIT and research for the Center for Army Analysis. He holds advanced degrees in Applied Mathematics & Statistics.
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Raju Gulabani, VP of Databases, Analytics & AI, AWS
Raju Gulabani is VP of Databases, Analytics & AI within AWS at Amazon.com. He is responsible for P&<, product management, engineering and operations for Database services such as Amazon Aurora and Amazon DynamoDB, and Analytics services such as Amazon Redshift and Amazon EMR, as well as AI services like Amazon Lex, Amazon Polly, and Amazon Rekognition. Prior to joining Amazon in his current position in 2010, Raju spent four years at Google and built the Google Apps business (now known as G Suite).Earlier in his career, Raju founded an Intel backed Wi-Fi Voice over IP company as well as held engineering management positions at Microsoft.
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Ryan Kelly, Data Architect, Equinox
Ryan Kelly is a data architect at Equinox, where he helps outline and implement frameworks for data initiatives. He also leads clickstream tracking which helps aid teams with insights on their digital initiatives. Ryan loves making it easier for people to reach and ingest their data for the purposes of business intelligence, analytics, and product/service enrichment. He also loves exploring and vetting new technologies to see how they can enhance what they do at Equinox
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Richard Boyd, Cloud Data Engineer, iRobot
Richard Boyd is a cloud data engineer with the iRobot Corporation’s Cloud Data Platform where he builds tools and services to support the world’s most beloved vacuum cleaner. Before joining iRobot, Richard built discrete event simulators for Amazon’s automated fulfillment centers in Amazon Robotics. His previous roles include cyber warfare systems analyst at MIT and research for the Center for Army Analysis. He holds advanced degrees in Applied Mathematics & Statistics.
-
Raju Gulabani, VP of Databases, Analytics & AI, AWS
Raju Gulabani is VP of Databases, Analytics & AI within AWS at Amazon.com. He is responsible for P&<, product management, engineering and operations for Database services such as Amazon Aurora and Amazon DynamoDB, and Analytics services such as Amazon Redshift and Amazon EMR, as well as AI services like Amazon Lex, Amazon Polly, and Amazon Rekognition. Prior to joining Amazon in his current position in 2010, Raju spent four years at Google and built the Google Apps business (now known as G Suite).Earlier in his career, Raju founded an Intel backed Wi-Fi Voice over IP company as well as held engineering management positions at Microsoft.
-
Ryan Kelly, Data Architect, Equinox
Ryan Kelly is a data architect at Equinox, where he helps outline and implement frameworks for data initiatives. He also leads clickstream tracking which helps aid teams with insights on their digital initiatives. Ryan loves making it easier for people to reach and ingest their data for the purposes of business intelligence, analytics, and product/service enrichment. He also loves exploring and vetting new technologies to see how they can enhance what they do at Equinox
-
Richard Boyd, Cloud Data Engineer, iRobot
Richard Boyd is a cloud data engineer with the iRobot Corporation’s Cloud Data Platform where he builds tools and services to support the world’s most beloved vacuum cleaner. Before joining iRobot, Richard built discrete event simulators for Amazon’s automated fulfillment centers in Amazon Robotics. His previous roles include cyber warfare systems analyst at MIT and research for the Center for Army Analysis. He holds advanced degrees in Applied Mathematics & Statistics.
Customer Highlights
Epics Games’ entire analytics platform runs on AWS. Billions of game events, like player interactions on the map, their accuracy, damage taken and dealt, and what resources they are using are all sent to AWS.
Epics Games’ entire analytics platform runs on AWS. Billions of game events, like player interactions on the map, their accuracy, damage taken and dealt, and what resources they are using are all sent to AWS.
Epics Games’ entire analytics platform runs on AWS. Billions of game events, like player interactions on the map, their accuracy, damage taken and dealt, and what resources they are using are all sent to AWS.
Epics Games’ entire analytics platform runs on AWS. Billions of game events, like player interactions on the map, their accuracy, damage taken and dealt, and what resources they are using are all sent to AWS.
FAQS
- Where is AWS Innovate hosted?
- What is the price of attending AWS Innovate?
- Who should attend AWS Innovate?
- How do I get the certificate of attendance?
Q: Where is this event?
A: This event is an online event, hosted by AWS on the INXPO platform.
Q: Who should attend this event?
A: Developers building data-driven apps; DBAs and data engineers who are building analytics infrastructure and data pipelines; Analysts and data scientists who are deriving insights that answer complex business quesions and building/trainining machine learning models.
Q: How much does this event cost?
A: There is no cost to attend this event.
Q: What are the prerequisites before attending the event?
A: There are no prerequisites for attending the event. We encourage attendees to browse the Database and Analytics pages on the AWS website to get a brief overview of the services available to them.