How Sonrai Analytics leverages ML to accelerate Precision Medicine (L300) | AWS Events

Summary

このプレゼンテーションでは、アイルランドのスタートアップソリューションアーキテクトであるジョナ・クレイグが、AWSのサービスを活用してがん治療の効率化を目指すスタートアップであるソナイアナリティクスをサポートしていることを紹介します。ソナイアナリティクスは、大量の医療データを扱い、機械学習を用いてがんの診断や治療の時間を短縮する取り組みを進めています。彼らはAWSのマネージドサービスを活用し、小規模ながらも技術的最先端を駆使して、医療分野におけるイノベーションを目指しています。また、プレゼンテーションでは、AWSのサービスであるSageMakerを使用して機械学習モデルをトレーニング、展開し、監視するプロセスについても説明されています。

Takeaways

• 🌟 ソナイアナリティクスは、AWS技術を活用してがん治療の臨床試験時間を短縮し、医療システムの効率化に貢献しています。
• 👔 ジョナ・クレイグはアイルランドのスタートアップソリューションアーキテクトとして、さまざまな規模のスタートアップをサポートしています。
• 🛠️ AWSのマネージドサービスを活用することで、スタートアップチームは限られたリソースを効果的に利用し、技術的な課題に取り組むことができます。
• 🔧 機械学習のループは、データの準備、モデルのトレーニング、モデルの展開、そしてモニタリングとオーケストレーションの4つの基本的なステップから成り立っています。
• 💾 ソナイアナリティクスは、ペットバイト単位のデータを扱うため、効果的なコスト管理システムとスケーラブルなアーキテクチャが必要です。
• 🧬 彼らのクライアントは、がんを治療するための新しい薬剤を開発しており、AIを活用して適切な治療法を特定しています。
• 🔬 コンピュータービジョンを使用した特定のユースケースでは、顕微鏡画像からがん細胞を検出するAIを開発しています。
• 🚀 AWSのサービススタックを活用して、トレーニングから推論までのプロセスを効率化し、モデルのパフォーマンスを最適化しています。
• 🌐 グローバルな展開が可能で、AWSのデータセンターを活用して、顧客ごとにセグメント化されたインスタンスを提供しています。
• 🛡️ データ保護とプライバシーに重きを置いたAWSサービスを使用することで、GDPRなどの規制に適合しています。
• 🔑 AWSのアクティベートプログラムを活用して、初期のビジネス開発と技術的な検証を迅速に行うことができました。

Q & A

AWS認定チャレンジとは何ですか？

AWS認定チャレンジは、AWSのサービスや機能を学ぶためのプログラムで、機械学習やソリューションアーキテクチャを含むAWSの様々な認定資格に挑戦することができる。

ソナイアナリティクスはどのような企業ですか？

ソナイアナリティクスは、AWS技術を利用してがんのドラッグ試験時間を短縮することを目指すスタートアップ企業であり、医療分野における効率性向上に貢献している。

Jonah Craigの現在の職務は何ですか？

Jonah Craigはアイルランドにいるスタートアップソリューションアーキテクトとして、ソナイアナリティクスなどのスタートアップ企業をサポートしている。

ソナイアナリティクスが取り組んでいるデータの種類にはどのようなものがありますか？

ソナイアナリティクスは、医療や生命科学分野のデータを扱っており、petabytesのデータを処理する必要がある。

AWSサービスの中でソナイアナリティクスが使用しているものには何がありますか？

ソナイアナリティクスはAWSのサービスを幅広く使用しており、SageMaker、Health Omix、Athena、Glue、Lambda、Fargate、ECSなどが挙げられる。

ソナイアナリティクスが取り組んでいるがん治療アルゴリズムの重要なポイントは何ですか？

ソナイアナリティクスは、精確医学を通じてがんの治療法を開発しており、アルゴリズムを用いて患者さんに適切な治療法を提供することに重点を置いている。

AWSのSageMaker Studioの機能は何ですか？

SageMaker Studioは、データの準備からモデルの展開までを一連のプロセスで管理し、機械学習エンジニアやデータサイエンティストが協力できる環境を提供している。

ソナイアナリティクスが使用しているAWSのサーバーレスコンピュートとは何ですか？

AWSのサーバーレスコンピュートは、コードを実行するためのサーバーの管理を必要としないコンピュートサービスであり、LambdaやFargateなどが該当する。

ソナイアナリティクスが直面しているデータストレージの課題とは何ですか？

ソナイアナリティクスは、petabytesのデータを効率的に管理し、コストを最適化する必要があり、S3のライフサイクル管理を活用してホットとコールドのストレージ間でデータを移動させている。

ソナイアナリティクスが開発しているAIアルゴリズムの目的は何ですか？

ソナイアナリティクスは、AIアルゴリズムを開発してがんの治療法を迅速化し、病理学者の作業を支援することで、患者の診断と治療の時間を短縮することを目指している。

Outlines

00:00

🎤 プレゼンテーションの開始とAWS認定の紹介

Jonah Craigがプレゼンテーションの司会を始め、AWS認定チャレンジに参加した理由とその意味を説明。彼はアイルランドのスタートアップソリューションアーキテクトとして、ソナイアナリティクスなどの顧客と協力し、スタートアップ企業をサポートしていることを紹介。ソナイアナリティクスは、AWS技術を活用してがん治療の臨床試験時間を短縮する取り組みを行っている。Jonahは、データ準備、モデルトレーニング、モデルの展開と監視を含む機械学習のループを紹介し、ソナイアナリティクスがこれらのプロセスを効率的に遂行していることを強調した。

05:02

🔬 ソナイアナリティクスのデータサイエンスとAI製品開発

Dr. Matthew Aliseがソナイアナリティクスのデータサイエンス部門の責任者として、自社のAI製品開発の経緯と取り組みを語る。元分子生物学者の彼は、データ分析の需要が高まってからバイオインフォマティクスに転向し、ソナイに参加してからAI製品を開発している。ソナイは、がん治療法の開発を支援するため、精密医薬品向けのアルゴリズムをAWS平台上で開発している。また、コンピュータービジョンを使用して、病理学者の助けとなるAIを開発していることを紹介。

10:03

💊 がん治療法のデータ分析とAIのビジネスチャレンジ

Matthewは、がんの顕微鏡画像をデジタル化し、AIを用いて病理学者が手動で行っていた作業を自動化する取り組みについて説明。AIの適用により、病理学者の不足と高コストの問題を解決し、スライドのデジタル化をリードする。彼らの戦略は、最小限の患者データを用いて特徴量抽出器を開発し、その後自動データ準備を通じてモデルの性能を向上させ、最終的に医療現場での利用を目的としたワークフローに統合した。

15:06

🛠️ AWSテクノロジーを使ったデータサイエンスの基盤構築

Jaredがソナイのエンジニアリングチームを率いており、データエンジニアリングとインフラストラクチャを構築していることを紹介。ソナイは、信頼性の高い研究環境を提供し、GDPRなどの規制に則って患者データの取り扱いを行っている。また、AWSのサービスを活用して、複雑で多様なデータタイプに対応し、分析と発見を通じて患者の安全性を確保している。

20:07

🌐 AWSサービスを使ったデータアーキテクチャの構築

Jaredは、ソナイがAWSサービスを活用してデータアーキテクチャを構築し、分析とAI/MLの分析をサポートしていることを説明。Health OmixやAthena、Glueなどのサービスを用いて、大量の生データを効率的に処理し、分析と可視化を行っている。また、AWSのサーバーレス機能を活用して、柔軟なスケーリングとコスト効率の良いインフラを構築している。

25:07

🚀 AWS生態系を活用したスタートアップの成長

Jonahは、ソナイアナリティクスがAWSの管理サービスを活用して、製品開発に注力していることを強調。AWSのサポートとアクティベイトプログラムを通じて、ソナイは迅速に成長し、市場で競争力を維持している。また、AWSのドキュメントとトレーニング資源を活用して、技術的な課題に対処し、新しいサービスを迅速に採用している。

30:09

🔮 未来の展望とAWSサービスの進化

JaredとMatthewは、ソナイアナリティクスが今後の焦点をHealth Omixに置き、生データの処理を効率化する取り組みを行っていることを紹介。また、ファウンデーションモデルの開発や大規模な言語モデルを活用した新しいアルゴリズムの構築を予定している。AWSのサービスが進化し続けることで、ソナイはさらなる技術的イノベーションを目指している。

Keywords

💡AWS

AWSはAmazon Web Servicesの略で、クラウドコンピューティングサービスを提供するAmazonの部門です。ビデオではAWSのマネージドサービスがソナーアイアナリティクスが開発するAI製品にどのように統合されているかについて語られています。特に、AWSのサービスを使用してがん治療の時間短縮に貢献している例が挙げられています。

💡マシンラーニング

マシンラーニングは、コンピュータがデータから学習し、判断や予測を行う能力を持つようにするアルゴリズムや统计モデルの分野です。ビデオでは、マシンラーニングが医療分野でのデータ分析やがん治療の効率化にどのように役立つかが説明されています。

💡ソナーアイアナリティクス

ソナーアイアナリティクスは、ビデオで取り上げられている特定のスタートアップ企業で、AWS技術を活用してがん治療の時間を短縮するAI製品を開発しています。彼らの取り組みは、医療業界におけるデータサイエンスの応用例として紹介されています。

💡データサイエンティスト

データサイエンティストは、統計学、コンピュータサイエンス、およびドメイン知識を活用してデータから洞察を得る専門家です。ビデオでは、データサイエンティストが医療データの分析やAIモデルの開発に関与していることが強調されています。

💡SageMaker

SageMakerはAWSが提供するマシンラーニングプラットフォームで、データサイエンティストがモデルを構築、訓練、およびデプロイするのに使用されます。ビデオではSageMakerがソナーアイアナリティクスがAIモデルを開発する上でどのように活用されているかが説明されています。

💡モデルトレーニング

モデルトレーニングは、マシンラーニングにおいてデータセットを使ってアルゴリズムを教育し、特定のタスクを実行する能力を開発するプロセスです。ビデオでは、モデルトレーニングがAIの開発サイクルにおける重要なステップとして触れられています。

💡データプリペアテーション

データプリペアテーションは、データサイエンスの分野で、データをクリーンにし、適切な形式に変え、機械学習モデルで使用するために適切に加工するプロセスです。ビデオでは、データプリペアテーションがモデルの性能に影響を与える重要な要素として強調されています。

💡モデルデプロイメント

モデルデプロイメントは、訓練されたマシンラーニングモデルを実際のアプリケーションやサービスに統合するプロセスです。ビデオでは、ソナーアイアナリティクスがモデルを医療業界に実装し、がん治療の効率化に役立つかた様子が語されています。

💡MLOps

MLOpsは、機械学習のライフサイクルを自動化し、効率化するためのフレームワークやツールの集まりです。ビデオでは、MLOpsがモデルの開発からデプロイメントまでを効率的に管理する方法として触れられています。

💡Precision Medicine

Precision Medicineは、患者の個人差を考慮して治療を個別に調整する医療のアプローチです。ビデオでは、ソナーアイアナリティクスがPrecision Medicineの分野でAIを活用してがん治療の時間短縮に貢献していることが強調されています。

Highlights

AWS认证挑战介绍，鼓励人们学习AWS认证，包括机器学习和解决方案架构。

Jonah Craig作为初创解决方案架构师在爱尔兰工作，与Sonai Analytics等客户合作。

Sonai Analytics使用AWS技术减少癌症药物试验时间，提高医疗保健系统的效率。

Jonah介绍了机器学习的基础概念，包括数据准备、模型训练、部署和监控。

Amazon SageMaker服务概述，它如何封装机器学习流程。

Sonai Analytics面临的挑战，包括数据存储、成本管理、训练自己的基础模型和业务需求的扩展。

介绍了AWS的AI服务，如Amazon HealthLake，适用于医疗保健和生命科学领域。

SageMaker Studio如何支持数据准备到模型部署的全过程。

Jared和Matthew分别从数据科学和工程角度介绍Sonai的工作。

Matthew的背景介绍，从分子生物学家到数据科学领域的转变。

Sonai Analytics的成立背景和它在精准医疗领域的应用。

通过计算机视觉技术辅助病理学家分析癌症组织切片的用例。

使用AWS服务进行数据存储、处理和模型训练的技术细节。

Jared讨论了工程挑战，包括数据工程和基础设施建设。

如何使用AWS服务处理和分析大规模生物技术数据。

Sonai如何利用AWS服务加速药物开发和临床试验流程。

Jared和Matthew强调了AWS服务在医疗保健领域的应用潜力和未来展望。

观众参与请求，包括对演讲的反馈和即将举行的工作坊信息。

Transcripts

hello folks can you hear me

okay uh if you can take a moment to get

seated we'll kick off our presentation

today um the first thing I'll say is uh

wearing this jacket is not a full choice

of mine but um it's tied in with the AWS

certification challenge so if you're

looking to educate yourself on AWS

certifications whether that's machine

learning or Sol architecture it's a

really great way to learn Hands-On um

how the AWS Cloud works so I'm going to

kick things off um my name is Jonah

Craig and I work as a startup Solutions

architect here in Ireland and I have the

absolute privilege of working with

customers like sonai analytics um and we

support startups whether they're like

two people uh in in their parents

basement or garage uh all the way up to

startups who have scaled to 100 or 200

plus employees

so it's really good fun and again sonai

have been on that journey and it's been

great to support them through last year

and this year so we like to take the

most Cutting Edge and and really bring

it uh to you today from an AIML

perspective why I love working with

sonai for two really key reasons the

first is they're using um AWS technology

to reduce cancer drug trial times I'm

I'm sure many of us in this room

including myself had had cancer effect

effect their lives in some shape or form

whether through family or friends and I

know every day when I have a meeting

with Jared or Matt it's extremely

motivating to to get stuck in and and

help them grow because they're bringing

efficiencies into the Healthcare System

I also love working with them because

from a technology perspective what

they're doing is truly Cutting Edge from

a startup team you don't have unlimited

resources and you really need to

leverage things like AWS manag services

and I'm going to let them talk that talk

through that and break that down

so my job today is to set a framework if

you like um for machine learning and

make it simple and you can use this

framework uh as to like listen to their

side of the story so the machine

learning Loop when we break this stand

to a foundational concept we start with

data preparation arguably the most

important part about any machine

learning problem it does not matter if

you have the most Cutting Edge

algorithms the data really is everything

so making sure you've got access to data

and making it machine learning friendly

is

key we then move on to model training

okay so this is where we select a model

and we want to take that data that we

have and start training it so the

expensive part of machine learning and

cost optimization really is a key part

to this and then when we're ready and we

picked the optimum model we can deploy

that into production and monitor it okay

so we can talk about things like Model D

Rift um which may mean over time maybe

the data that's coming in is slightly

different to what it was trained on and

this is where the secret sauce kind of

comes in which is orchestration mlops

okay and our Amazon sagemaker service

encapsulates this Loop so if you're

building a machine learning problem you

can do all of this and chain it all

together and sonai again have these

efficiencies in place and what they can

do with a small team is is just uh

breathtaking I'm going to outline as

well the the key high level challenges

for sonai the first one is data storage

so sonai analytics are in the healthcare

life science space and they're taking

the this foundational model Trend and

really you know bringing it to to this

space in this vertical so they're

dealing with petabytes of data so

ensuring that they've got a an effective

cost uh management system for that is is

crucial and also to be able to handle

that scale is key they're also um

training and hosting Their Own

Foundation models which is again

extremely Cutting Edge they're using

services such as Sage maker to help with

this but I'm going to let the the guys

dive deep into how they're actually

doing this and then finally scaling to

meet business demand it's really really

important if you're a startup or an

Enterprise you need to be able to scale

your technical architecture to be able

to meet customer demand and again sonai

can do that if they have a a big

Enterprise client or whether it's a new

startups joining their system they can

do that because they've architect

detected it in the correct

way so you'll hear a lot today about our

AWS Services okay and today we're going

to focus on a bit on Sage makers so keep

that in mind through their presentation

and we have a whole host of AI Services

Okay so we've got Amazon Health omix

here which uh again is it applies mostly

to the hcls space however no matter

you're vertical whether you're in

fintech or anything like this there are

AI Services there that can do a lot of

heavy lifting of AI in the back end okay

so I just wanted to paint that in case

you're maybe not in the htls space but

there's a lot of things that you can

take away from our service

deack so let's talk a little bit about

sagemaker before I hand over again if

you keep that model machine learning

Loop in mind you can see it sagemaker

Studio takes care of everything from

data preparation all the way through to

the deployment of the model and what's

really helpful is that it is designed

for a whole host of uh people using the

service so ml Engineers or data uh

scientists they can all collaborate in

one place which really helps

efficiencies because you're not using

say 203d Party Services for each part of

this process again the results can be

like sonai the way that they handle

this from an AWS architecture standpoint

I get asked all the time like this

sounds great but how do I actually get

started with something Hands-On that I

can build for my own company so again I

always Point people in the right

direction the best place to start is

with a workshop get hands on and this QR

code will take you to the sage maker

immersion day and this again we've got

examples of if you want to just maybe

experiment for your first time with sage

maker or you want to build a pipeline

and orchestrate a whole end to end

solution get started we have um uh we

then encourage you to build a POC on

your side and we have AWS Partners I'm

sure you've heard a lot about them today

so again don't be afraid to engage them

they they're they've done these problems

before and they can help you deliver

them and teach your team so again really

fast way to do it and then straight into

production so I wanted to set the scene

for sonay um I'm going to introduce uh

Dr Matthew Alise who is the uh head of

data science and it's been a pleasure

working with you Matt and uh I'll leave

it to

you afternoon guys uh you can hear me

great okay um so I'm going to set the

scene a little bit more than what

Jonah's done thank you so much for for

the introduction and also the invitation

to to present uh so we're doing a double

act today uh I'm doing the data science

and my colleague Jared is doing the

engineering we're both sides of this the

same coin okay uh and I can't do my job

without him and vice versa I build the

ml I'm extracted away from the

infrastructure and Jared essentially

does the plumbing uh for us so that we

can get access to data and train our

models so I'm going to tell you a bit

about what what we do and why we do it

and how we do it but first off I'll give

you a little bit about about myself and

my path to data science um I I actually

originally started as a molecular

biologist in the lab about 10 years ago

so I was probably looked a little bit

more like a mad scientist not I wasn't a

gold jacket but it was it was a long

white one of test tubes and mixing

Regents and I worked in a Precision

medicine lab uh back then there was a

lot of new technologies about sequencing

DNA proteins and so on and what happened

about 2015 uh which coincides with the

Advent of you know Ai and Nvidia

Technologies and so on is that we were

generating so much data that the

bottleneck wasn't the generation it was

the analysis and the utilization of it

so I then transitioned into what's

called bioinformatics uh which is really

just a subset of data science within the

health field uh so I joined son about 5

years ago and we' we've been building AI

products ever since uh so now now we can

dive in to sonre um so sonre is a Queens

University spin in Belfast it was a

fairly long drive down today uh up early

uh but it's great to be down here uh and

what we've developed is a number of

different algorithms hosted on AWS

platforms for our clients uh our clients

are developing drugs to treat cancer uh

within prec Precision medicine the

important thing is who should get the

drug you don't give everyone the same

drug because drugs can't be toxic and

there's no point in giving a drug to a

patient with an O under respond so

identify new markers to apply the right

treatment and we're develop helping

develop Diagnostics or digital

Diagnostics using AI ultimately to

enable our clients to save patient lives

so that that's sunr why do we do it uh

Precision medicine includes a lot of

different disease types uh but one of

the main ones that we focus on is cancer

uh I don't think I need to tell everyone

in the room that cancer is bad okay it's

it's very unpleasant one in two people

will develop a cancer incident in their

lifetime hundreds of thousands of people

die across the UK and Ireland every year

but as I said on the previous slide

there are a lot of organizations

developing therapies to help patients

and the amount of data that they're

Genera in the lab is very amable to AI

there's a lot of digital transformation

happening across biotech and right

through the healthcare and we're here to

help so I'm going to Spotlight a use

case uh focusing on computer vision uh a

little bit like the uh the previous talk

but uh fre it in the business context

and then dive into the AWS Tex stack

that we actually used right from

training through the machine learning or

the inference sorry uh and I'm going to

pass over to my colleague Jared who's

going to Spotlight some of the the

engineering challenges behind that as

well and it's also going to really tie

in nicely with the the train in Loop

which Jonah highlighted as

well uh so this particular use case uh

I'll actually point to the images first

that you can see so the top image there

is actually a microscope a glass slide

of a patient's cancer this is colong

cancer it's a glass slide and for a long

time it was what we refer to as analog

you looked on a microscope the pathology

just would be able to say okay there's

tumor uh they would get a Sharpie pen

literally a Sharpie pen draw on the

glass slid and say that's where you need

to extract your DNA from or your RNA or

whatever it is so that you can have a

test on your

diagnosis but we're now starting to scan

these slides so they're digitized

they're really large images they're

pyramidal tiffs you can see as you from

the the image below but because they're

not digitized it's aable to Ai and we

can essentially replace that Sharpie pen

with our own AI generated proposed so

it's called a region of Interest this is

important for a number of reasons

because Pathologists are scarce there's

actually fewer and fewer Pathologists as

uh as we go through time and they're

very expensive it's about $100 per slide

there and you'll see that as the AI that

we apply is is incredibly cost effective

so that's the business challenge we ask

ourselves can AI help with this spoiler

alert it can uh but technical

requirement is can we train a model

entirely within AWS detect cancer from

these large images and assist

Pathologists now we work in a regular

environment we're one of the few

companies building AI as medical devices

uh so assist is an important word that

we're not replacing Pathologists uh and

the business output of this is to save

millions in operational costs if there's

hundreds of thousands of those images

and you're paying pathologist $100 ago

in

mindset so what was the strategy uh we

started with the minimum amount of

patients that we could really work with

to generate a feature extractor and uh

generate a model so we had to ingest a

large data set 2,000 patients actually

quite a lot I know that in other domains

that's a very small number but 2,000

patients actually quite hard to get a

hold of uh then we had to automate the

data preparation so this data came from

the lab weo automate that and then we

enter that train and evaluation Loop and

we iter it and once the model is reaches

a sufficient performance uh then we can

deploy it with an user workflow and

that's the important part a model on its

own is not useful it has to be in an

adoptable workflow for uh uh for

clinicians to use and ultimately what we

got out of this was a model which

outperforms our competitors which is

great it's scalable Cloud native

deployment so we can actually scale to

meet the needs of healthcare uh so it's

not one patient at a time we can scale

using serverless comput to uh to to go

acrost thousands of patients uh the

turnaround time Jared's actually going

to speak to we've got some really

impressive metrics there as well as the

cost savings for clients but now I'm

going to to spot L an AWS conference so

I'm going to speak to some of the uh the

technology behind our training

um so remember these are Precision

medicine Labs so the training data

actually comes from these pathology

instruments we set up an Amazon SFTP uh

it lands an S3 and then Jared's team

have an an event driven architecture in

place which uh uses lambas to extract

the metadata from these really large

images so that they can be viewed by

Pathologists so our machine learning

pipelines are a little bit different

from other domains in the sense that

Pathologists have to be able to look at

the images before they can go to the

next stage so a human in the loop is

really important uh you might need 10

pathologist a thousand pathologist

looking at different images we're also

going to talk through some of the really

cool uh lamba based architecture that uh

or use of lambdas for viewing these

images these really big images uh the

next step on the data preparation side

uh is tile extraction so you saw tiles

within those images uh you cannot pass

this single image these large images

through a GPU they're just too big so

what you need to do is tile them up or

chunk them up uh and that the pipelines

that we actually originally started with

on premise was incredibly slow uh we've

used Cloud native technology uh you can

instantiate thousands of instances so

that it's done in in in hours instead of

days or even months that's the data

preparation side the data then lands on

S3 uh we use fargate tasks orchestrated

by ECS to do that uh and then the data

is ready for us to to start training and

that the ml team can get stuck in uh in

terms of we we have our docker for

training uh parked on ECR and we use

Sage maker uh for for our training jobs

uh we do we did have an on- premise

solution which we utilized a lot but

there's I don't think I need to tell

people why using the cloud is really

important for scalability but it means

that we didn't have cues machine

learning Engineers waiting for GPU

access uh and at a startup that's really

important we can also scale down uh our

use of compute as needed uh we can scale

our experimentation we get our metrics

we output our model back to S3 one of

the things that we're looking to do is

move to the model registry as part of

sagemaker uh we do have our own uh

artifactory on S3 to handle the nuances

of what we do and because we build

medical devices it's just about timing

to move towards uh embracing uh some of

the new features in stag maker so the

model performance once it's reached that

stage it's there is a lot of rigor that

goes into training some of these models

we follow the fda's gmlp so good machine

learning practices uh we have to invol

people right from oncologists machine

learning Engineers doctors uh even

nurses and survivors where we can input

about this the sign off of the

performance of this model once that's

done we can start to get ready for a

production uh and this is where our

inference comes in into play so uh our

S3 model artifact approved for

production it's baked into an inference

container uh it sounds like common sense

but we are not allowed to have any

training logic in our inference

containers whatsoever uh most people

would not but we're actually mandated by

the regulatory bodies that that cannot

happen there can be no Superfluous logic

in there in the inference containers so

we have to have a separate one uh that's

uh hoston uh ECR it's built and that's

when I start to from my perspective our

team start to think about handing over

Jared where his team will ship this out

ship the code uh cdk is used for INF the

infrastructure we have blue green

deployment so we can bring one uh one

application down uh and wait for the

other and we use serverless comput so uh

Lambda triggers uh and the fargate ECS

fargate task orchestrated by ECS uh we

are going to be moving to inference on

Sage maker uh the those tasks do not

have GPU yet uh we want GPU so that we

can further improve the turn around time

so Sage maker is what we'll be doing uh

for inference our serverless compute as

well uh the final part is monitoring

that's very important to me as well I've

greatly simplified all of the

infrastructure in place for that that's

not just watch but it's a really good

place for us to hone into uh we need to

understand the usage patterns of our

algorithms uh and Jonah mentioned model

drift metrics uh so if we start to see a

jump from 20% cancer incidence to 40% it

might tell us there's something wrong we

need to go to the lab understand if

something is changed with the data

that's being produced all of these

insights allow for next iterations and

training a a more robust model uh so

that's a really good segue for me to

hand over to to Jared my counterpart

who's going to uh talk about the

engineering challenges around what we

do thank you Matt thank you Jonah um I'm

Jer loan I lead the engineering team

that's a team of cloud Engineers full

stack and quality insurance Engineers as

Matt said we basically handle the

plumbing which is the data engineering

and the infrastructure that underpins

the AI and machine learning analysis uh

and I'm going to Spotlight how we use

edus at a high level what we provide to

your customer customers we provide we

build our own algorithms but our main

job is actually giving Pharma and

biotech the tools for them to develop

drugs for them to develop new

treatments um so our primary use cases

that we deploy was called The Trusted

research environment that is a prepr a

prepackage Cloud environment that allows

them to have all their tools all their

data all their users in a regulated

environment that's really important this

soner or infrastructure is heavily

dictated by things like gdpr things like

how we handle patient data and it was a

big reason why we also picked AWS so

that we can use certified data centers

and where we can trust that our data

will be

protected um our customers are seeking

to discover new cancer drugs new

treatments um they they want to develop

these models and algorithms they want to

typically take their data assets through

the clinical trial so they can get to

Market and we cover the full end to end

life cycle of that and this means

handling three sort of key areas so on

the Discovery side it's about how we

actually analyze the complex data it can

be really big really un weedly and

unlike business data it's way more

varied it tends to be messy you tend to

have very Niche modalities that require

completely different tools and this forc

us to look at a data M architecture and

to look at a variety of different AWS

services that suits each individual data

type so for example health omix is very

good at handling bulky data directly

from Lab instruments um it can use

popular framework such as nextflow which

is really important for handling all of

the different types of data that can

come out whether it's tabular genomic

protein Imaging based data we use Athena

as an extremely powerful um serverless

uh query that we can build to run under

analytics and our visualizations and we

can use glue to handle things like ETL

converting CSV to parkz and typically as

as a general process we need to take

large bulky data that for example might

be 100 gigabyte of raw data which you

then turn into 40 terabyte of working

data and then you end up with actually

surprising small csvs in parkz which is

resolved and that's where you can make

analytics and findings but you need to

link it all together you need

traceability and you need to have a an

auditing basically so that we can we can

know the full history of what happened

to this sta we can know the proper

process was carried out because

ultimately patient safety is at

risk um after we make our discoveries

and handle our data we now want to

develop it so sonre and our customers

make a lot of use of sage maker and

sagemaker notebooks and we can use that

to directly hook into other AWS services

including running llms but our primary

use is spinning up applications um

training tun in models and if preparing

new drugs treatments or algorithms for

eventual clinical release on the

deployment side um we have two use cases

our customers need to deploy the things

they build so we can give them an

operational window where they can use

things like API Gateway Dockers and Apps

sync so they can spin up their compute

they connect their data sources but also

on a very practical level we need to

deploy across the world we need to have

a partner like AWS who has data centers

that we can trust and we can use AWS

orgs to create a separate account for

each and every client it's fully

segregated we do not share anything

there's no risk of databases being um

coming across people sharing compute

every single customer gets a completely

different instance and we can use

control toar to completely automate the

deployment of it we can monitor remotely

we can move it updates and we also um

use cdk which is the cloud development

kit and that lets us treat the cloud

infrastructure the way we treat our

JavaScript code is completely PR it's

completely controlled we have absolute

certainty of what version is going out

and that each customer is using the

correct

version um spotlighting some interesting

challenges and I think Emily did a great

job pointing this earlier as well there

going to be a lot of um things that

resonate with us um I'm going to walk

through a real use case um with a client

who was looking to hold a paby or a

th000 terab of Imaging data um these

images are about 5 to 12 GB each and

when you extract them they're more like

50 gab so they get very unwe very

quickly and the big challenge with this

type of data is you need both High

availability and low cost and those

things are to typically at the opposite

ends of the spectrum and the way that we

handled that was to use S3's native life

cycle management so we're able to very

easily be by apis as well as by

automatically triggering after say 3

weeks of inactivity we can cycle data

between what we call Hot which is when

it's an S3 standard that's when a

pathologist needs to view the image they

want to run model training they want to

execute algorithms so we need it highly

available and then cold is kind of like

a having a a a cold storage where maybe

you've already processed the data but

legally you need to hold it or maybe

you're not ready to analyze it you want

to batch it later and a good way we like

to think about this it's very similar to

a manufacturing term known as just in

time where when you have a physical

Warehouse you don't want to hold

inventory for the sake of it you want to

be very e efficient about when you bring

it in about when you load it out it's

how you you can make a significant

amount of cost savings and in this case

alone this led to 600,000 in savings

just from holding the data one of the

first surprising things when I when I

joined soner was that the DAT the

storage is going to be a bigger cost

problem compute which I really did not

see coming but once we get into this

scale it it it it really shows that um

and interestingly we could have made

more cost savings cuz we could have got

it down to 25 or 50 terab hot and not a

game would have had a dramatic

impact um another interesting challenge

is son you fac when we were building our

AI um soon to be CI video our medical

devices is that we were using on-

premise server as Matt mentioned and in

order to handle this data we need to

pull the individual SVS orti files we

need to

extract the metadata we need to pull out

5 to 600,000 individual tiles store them

prepare them for training it's just very

computationally intensive and we had a

pretty expensive on- premise server but

ultimately we were just bottlenecked by

CPUs and gpus and based on the current

Cadence we were looking at six-month

delay just to get the data ready just to

do the data preparation stage so to get

around this we created an MVP Cloud

native tile extractor It Was Written in

Rust and it used lambas and farget and

the the end result was that we basically

just dumped all of the images into 1 F3

bucket we set up an automation policy to

then spin up um an individual task fre

image and six months became six hours it

just iterated through the full list it

gave us everything we need and on a

practical level for sunr it was a

difference in meeting the deadline and

feeling but it also means that us and

our customers can now build these things

6 to 12 months faster and this scales

with the data so the more data you have

the more this is more important it was

also incredibly cost effective um way

cheaper than the cost of electric would

have been to run our

server uh another interesting challenge

um that Madden mentioned was that we

have to keep the human in the loop so we

can't just hold this data it can't just

be behind the scenes and it would be a

lot cheaper and efficient if it was just

spinning up boxes in the back we

actually need to make it available and

Matt had mentioned about working in a

lab we essentially provide a digital

microscope so we need to make those

images available on a web browser um and

if you can imagine from the image in

that you can you can zoom left you can

zoom in and out you can go left up down

right

and when you're doing that you are

essentially trying to view 50 gab worth

of Imaging data there's about 5 or

600,000 images they're about 80

kilobytes each we're also trying to

avoid not having to store an extra 50 CU

then that one pyte now becomes 10 pyte

when the time you fully extracted so

what we did was create a cloud native

image viewer where we spun up thousands

of Lambda indications we linked it up

with appsync for graph query to a

JavaScript front end and essentially we

are lazy loading and rendering ing as we

spin up a Lambda it reaches in to the

archive format pulls out the tile that

we need we're not technically holding N3

storage we're not even paying S3 cost in

that because it's just a a temporary

transfer in a live session and the

result is is pretty much instantaneous I

can pan around I'm lazy loading as I

move around the screen I'm only using

the data that I need it's entirely

serverless it's it's it's instantaneous

as well and uh it's also extremely cost

effective we're talking fractions of a

penny for things like this to run over a

couple of hours

and a big thing for sonry was as well

that a lot of Open Source Solutions

competitors to get around this this big

data storage problem they sacrifice

image quality they heavily compress the

image because how else do you get that

amount of data on on a server um we were

able to completely avoid that we we

don't remotely compress we use the full

original size image and for us that is

the difference potentially between an AI

correctly estimating whether you have

lung cancer or not so we have we had no

sacrific the quality but we had

instantaneous speed and we avoided

serious storage

costs um this is now looping back to

sort of Matt's key purpose about why we

do this um I've worked in healthcare a

long time I used to build medical device

software in hospitals as well Healthcare

basically has a scalability problem in a

nutshell there are constantly more and

more patients there's fewer doctors

there's fewer nurses there's fewer path

Pathologists there's less money to go

around for patient and that is not going

to get better anytime soon so lots of

companies like our ourselves are looking

at how do we digitize this how do we use

Ai and Cloud to automate and a very

telling example is our MSI algorithm

that we built um to detect colon cancer

the current gold standard is about two

weeks in a lab it goes through multiple

people there's lots of physical

processes involved we can do the

equivalent in 3 minutes and in fact in

the space of time that I've been

speaking to you we could have done the

entirety of Ireland and it would have

been surprisingly cost effective we

would have just run Millions at a time

and tested it to 100,000 at a time a has

handled it absolutely no problem uh and

also then in terms of the Practical

stuff we mentioned the Pathologists need

access to a thousand terabytes and

pedabytes of data at a time the current

reality is they're passing around

physical hard drives they're losing data

they're struggling to find things we can

we can hold that data we can make it

highly available and it means the

pathologist can go through more cases

which means more patients can get more

diagnosis and of course everything we do

has to be regulated governed and all

about patient safety and we an ISO 1345

certified company so we've we've had

external guidance on how we do machine

learning what our practices are and that

we are prioritizing patient safety and

and positive cancer

outcomes uh and I want to talk a bit

about uh aeds and why we chose them and

we're a very young startup we're 5 to

six years old so we had a fresh chance

to look at providers and one of the big

things that appealed to us was that AWS

has a massive ecosystem um there's so

many services that we can latch on to

and I think as Jonah mentioned at the

start of a speech we we're a small but

ambitious team we can't boil the ocean

we don't want to build things that we

don't need to build we want to build the

things we care about so for example we

didn't need to build our own

authentication system we can use Cognito

we didn't need to build ways to manage

certain infrastructure when we can use

Athena we can use homic this allows us

to be really precious with our time and

it's ultimately why we compete against

bigger companies because we are picking

the things that matter um another key

thing is I mentioned is that we deploy

across the world so having a large cloud

provider that has respected data centers

where we can reliably ship out the same

product it's just fundamentally required

and the flexibility of AWS is also

really useful there's always aund ways

to do things it gives us the freedom to

build the things that we want I also

can't uh speak highly enough about um

the support we've had from Jon and sinon

um we torture them regularly and other

members of the edus team we've had

access to training for sage maker we've

had beta access to health omix it allows

us to get a competitive Head Start um it

whenever we're having trouble get

product Specialists brought in who can

help us identify if our cyber security

is up the spec it can make sure that

we're doing things the right way it just

allows us to move fast and build fast

which we have to do we have no choice

but to be moving quickly in the market

and in order to H get to where we are

today we took advantage of aded activate

um in the early days it gave us um the

ability to explore and create pocs and

minimum valuable products it meant that

we could afford to experiment and we

could get something in from the

customers it was really critical to our

early early business days um and in a

similar V we can look at Marketplace as

a way to see do we need to build this or

can we buy it can I take something off

the shelf can I deal with this awkward

customer request is this what sonra

really wants to do if not let's just

find it and Slot it in and that way we

get to keep moving we get to protect our

road map which again is just really

important for a company of our size uh

and lastly probably a fairly undervalued

statement AWS does tend have really good

documentation we're constantly trying

new things we're Hing our heads against

the wall so being able to access blogs

videos training material it's the

difference between meeting in a deadline

and

not um so final slide for me and I just

want to talk about the future and

there's some things we've already

pointed to um health omix is probably

the biggest Focus for us this year we've

already got it up and running and we've

had a lot of great support from The A

Team in that as mentioned earlier it's

for handling bulky raw data that tends

to come from instruments it's a really

big problem to our customers and in

practice you can be dealing with um

gigabytes of data that terabytes when

you're using it we actually went to

three different prototypes it's still in

the early days but um we went with AWS

batch originally which is the most open

source recommended route and we had the

exact same run cost $2,000 and health

omix came in at

$22 and you know that's because of

things like there's omix compute you're

avoiding networking and data cost with

not gateways so there's lots of

advantages and there's also security and

infrastructure benefits that we don't

have to manage infrastructure we can

rely upon apis and it just makes our

life easier um thankfully I don't have

to explain Foundation models because

Emily gave a fantastic presentation on

it but this is something that son very

much is wanting to double down on um

Imaging is very complicated I mentioned

about the expensive issue with um data

storage and how tile extractions very

competition intensive that gives us now

a foundation to build a foundation model

it means that we can potentially develop

this as a way to rapidly build new

cancer diagnosis algorithms and to make

what we call multi-indicator

multi-indicator based so it could be

that we can more easily um identify

other parts of cancer in the body as

well as allow our customers to build

these things as well and then last but

not least everyone on the plan has been

talking large about large language

models sonra has used them also to try

and and boost Discovery and we are

working with our own clients to see

about how we can link in apis to bedrock

so that customers can basically get new

drugs to Market quicker that that's our

purpose is to constantly speed up to

reduce the barriers overcome the

technical hurdles uh thank you very much

I'm going to pass back to Jonah

[Applause]

Jared Matthew absolutely incredible like

it's just seeing what you've achieved

with the team um is just brilliant and

every month I check in with you folks

like it's a new story and you've

progressed something that may take you

know a year so really really appreciate

it and definitely note that what what

sonai analytics do extremely well is

they really lean into the AWS manag

services so say maker Lambda fargate

they're using that and what that means

is they can focus their time developing

their own product and business needs and

less time managing kind of

infrastructure so you can see that and

the way they're going again talked a

little bit about the AI Services check

them out uh there's loads of general

ones there's verticalized ones and

there's a whole stack you can easily

find them on the AWS website um I have

uh one final ask for the audience um it

takes about 30 seconds if you wouldn't

mind scanning the QR code it helps uh

speakers such as myself Jared and

Matthew uh talk at these events so would

really appreciate uh the feedback from

yourselves it it should only take 30

seconds so on a final note um from a

startup perspective if anyone wants to

get handson with uh some generative AI

kind of workshops and things um we are

running a generative AI Workshop uh in

Dublin on the 22nd of May where you can

bring yourself and a and a team and

we're going to build a generative AI

chat bot so if you want to get involved

with that can find me after the talk and

Jared Matthew and myself are going to be

at the startup uh Loft over there so

when you go out the door turn right

we'll take any questions if you have any

you want to learn from the two or myself

uh we'd be really happy to to Dive Right

In with those conversations so we'll be

there for about half an hour thank you

so much for being in a great audience

[Applause]