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Embracing Cloud Deployment for Big
Data and DevOps
Steven Woodward
Technology Incubation Lab, CTO, AstraZeneca 22nd June 2...
CTO Office
2
Our team was established to create new value by catalyzing innovative,
emerging technologies across AstraZene...
Global Reach
Today we have 3 tech labs.
Presence in these key technology
clusters gives an early view of
emerging technolo...
Scaling for Big Data
200m+ Unstructured Documents
5
We have silos of unstructured content, both inside the company and in the cloud.
We initial...
Big Data Engine
6
We had developed a big data engine that powers multiple business
applications, not just enterprise searc...
Containers for Rapid Deployment
Elastic Cloud Performance
8
Sinequa now handles ~2500 queries per min. Designed from the start to
leverage cloud elastic s...
In-Video Search!
9
The Azure Media Services cloud platform is able to ingest audio and video files and
automatically gener...
From Code to Container
10
We can write code in an IDE,
commit to Git Repo, Build a
Docker Container, test and
deploy to AW...
Exponential Data Growth
Wearables, Sensors and Beacons
12
• Disparate sensors form
cohesive systems
Artificial Intelligence
1313
Skills and Expertise Lookup:
Department Information Delivery:
Information Lookup:
• Uses mach...
Predictive Modelling
14
With larger, complex datasets, machine-learning techniques can accelerate and increase
accuracy of...
Confidentiality Notice
This file is private and may contain confidential and proprietary information. If you have received...
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Embracing Cloud Deployment for Big Data and Dev Ops

Presentation by Steve Woodward, cloud solution engineer in my team at Cloud & DevOps World in London on June 22nd 2016. Overview about how we architect our cloud solutions using emerging technologies with elastic scaling, docker containers and novel services that our customers can use quickly - from sensors & streaming lab data, to predictive modelling and artificial intelligence.

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Embracing Cloud Deployment for Big Data and Dev Ops

  1. 1. Embracing Cloud Deployment for Big Data and DevOps Steven Woodward Technology Incubation Lab, CTO, AstraZeneca 22nd June 2016
  2. 2. CTO Office 2 Our team was established to create new value by catalyzing innovative, emerging technologies across AstraZeneca Technology Incubation Labs Competency Centers Enterprise Architecture Multi-disciplinary teams that test new technologies and accelerate platforms to build internal expertise and hands-on experience of potential game-changing technologies whilst focusing on immediate business problems Established User Experience and Mobility competency centers as key strategic areas for future success. They will develop and be embedded into enterprise capabilities with world-class technology leadership Providing enterprise leadership to understand the pain points of the business and ensure that proposed technology changes are unified and governed to maximise business value as AZIT develop corporate platforms, workflows and choices
  3. 3. Global Reach Today we have 3 tech labs. Presence in these key technology clusters gives an early view of emerging technologies, companies and start-ups. San Francisco is today’s Innovation Capital of the World and our tech lab will facilitate links with innovative research start-ups, venture capitalists and global technology leaders. This office provides new opportunities at the forefront of healthcare digital innovation and major breakthroughs in enterprise technology. Cambridge is the most dynamic scientific business cluster in the world. We are surrounded by 19 science parks with over 1,500 high-tech companies, world class academic institutions at the bleeding edge of scientific research and key research hospitals like Addenbrookes. Shanghai is emerging as the top city for tech innovation internationally and is predicted to become the global technology centre within 4 years. Our tech lab is able to tap into novel scientific research & development, advanced engineering, health nanotechnology and robotics.
  4. 4. Scaling for Big Data
  5. 5. 200m+ Unstructured Documents 5 We have silos of unstructured content, both inside the company and in the cloud. We initially focused on developing a big-data engine for unstructured R&D content for scientists. Admin Access Data Sources Data Mappings Configure Permissions Tag Content Sinequa Index Applications Web Service
  6. 6. Big Data Engine 6 We had developed a big data engine that powers multiple business applications, not just enterprise search. This Swiss-army knife for search let’s us tackle many problems R&D News AlertsR&D ChemSearch Find Partners Mobile Apps Competitive Intel Medical Affairs
  7. 7. Containers for Rapid Deployment
  8. 8. Elastic Cloud Performance 8 Sinequa now handles ~2500 queries per min. Designed from the start to leverage cloud elastic scaling capabilities for responsive performance for 10 users or 70,000 users. 8 Happy UsersDockerElastic Container Services By leveraging container technology, we can spin up new services quickly. It’s very cost-effective and enables new approaches to be easily included in your workflows  Automated video transcription  High precision text analytics
  9. 9. In-Video Search! 9 The Azure Media Services cloud platform is able to ingest audio and video files and automatically generates a >90% accurate transcription (English only) 9 By combining Amazon, Docker and Microsoft Azure, we were able to go from first prototype to global implementation in 7 weeks in our Enterprise search platform.
  10. 10. From Code to Container 10 We can write code in an IDE, commit to Git Repo, Build a Docker Container, test and deploy to AWS in about 3 minutes!
  11. 11. Exponential Data Growth
  12. 12. Wearables, Sensors and Beacons 12 • Disparate sensors form cohesive systems
  13. 13. Artificial Intelligence 1313 Skills and Expertise Lookup: Department Information Delivery: Information Lookup: • Uses machine learning and neural networks to help bring context to user input • Users a series of natural language processing algorithms to help validate and clean the input data • Connects to AstraZeneca API’s to gather individuals skillsets • Built as a service, allowing multiple input channels • Slack • Chatter • Skype
  14. 14. Predictive Modelling 14 With larger, complex datasets, machine-learning techniques can accelerate and increase accuracy of decision making, improving our productivity Optimised models created using cloud elastic approaches offer another big data engine opportunity across the enterprise:- salesforce optimisation, employee retention, resource allocation, patient responder prediction, manufacturing/supply chain improvements.
  15. 15. Confidentiality Notice This file is private and may contain confidential and proprietary information. If you have received this file in error, please notify us and remove it from your system and note that you must not copy, distribute or take any action in reliance on it. Any unauthorized use or disclosure of the contents of this file is not permitted and may be unlawful. AstraZeneca PLC, 2 Kingdom Street, London, W2 6BD, UK, T: +44(0)20 7604 8000, F: +44 (0)20 7604 8151, www.astrazeneca.com 15 We are currently scouting Federated Analytics, Mobile Security, Video Compression & Optimization, Pre-emptive event driven actions….Please get in touch if you can help Thank You & Questions! Steve Woodward steven.woodward@astrazeneca.com Nick Brown nick.brown@astrazeneca.com

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