"Big Data & Machine Learning Innovation with Google Cloud Platform", Shira Kimchi Google Cloud Platform, Business Manager of MEA
•Descargar como PPTX, PDF•
8 recomendaciones•3,813 vistas
"Big Data & Machine Learning Innovation with Google Cloud Platform", Shira Kimchi Google Cloud Platform, Business Manager of MEA Watch more from Data Natives Tel Aviv 2016 here: http://bit.ly/2hw1MY0 Visit the conference website to learn more: http://telaviv.datanatives.io/ Follow Data Natives: https://www.facebook.com/DataNatives https://twitter.com/DataNativesConf Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS About the Author: Shira is the Business manager of MEA Google Cloud Platform, responsible for business strategy and growth of Google Cloud Platform solutions across Middle east and Africa.Shira has a rich technical background across Infrastructure and cloud services, working with Startups and Enterprise organizations. Prior to Google, Shira was an Account Executive at Microsoft leading the relationship with key Hi-Tech enterprise accounts. In the last few years her business focus is Cloud - including SAAS|PAAS|IAAS solutions that answer the business needs of various customers.
Recomendados
Recomendados
Más contenido relacionado
Más de Dataconomy Media
Más de Dataconomy Media (20)
Último
Último (20)
"Big Data & Machine Learning Innovation with Google Cloud Platform", Shira Kimchi Google Cloud Platform, Business Manager of MEA
- 1. 1Data & Analytics 3rd Generation Data Platform: From Buckets of Bits to Understanding
- 2. 2Data & Analytics Data is Everything. How well you use your data can determine the degree of your success.
- 3. 3 Street nameStreet number Street View Sign Business facade Sign Business name Traffic light Traffic signStreet number
- 4. 4Data & Analytics 3rd Gen Data Platforms Challenges Data access to a variety of data sources. Develop and build analytic models. Data preparation, exploration and visualization. Deploy models and integrate them into business processes and applications. High performance and scalability for both development and deployment. Perform platform, project and model management.
- 5. 55 – Doug Cutting, Hadoop Co-Creator “Google is living a few years in the future and sending the rest of us messages”
- 6. 6Data & Analytics Google Cloud Platform Vision Single-node computing “Some assembly required” True, on-demand cloud An actual, global elastic cloud 3rd Wave Invest your energy in great apps Colocation Your kit, someone else’s building. Yours to manage. 1st Wave Today's Cloud: Virtualized Data Centers Standard virtual kit, for rent. Still yours to manage. 2nd Wave
- 7. 7Data & Analytics Bridging the Waves AnalyzeStoreCapture BigQuery (large scale SQL) Cloud Machine Learning Cloud Pub/Sub Logs, App Engine BigQuery streaming Process Cloud Dataflow (stream and batch) Cloud Storage (objects) Cloud SQL (SQL) Cloud Datastore (NoSQL) BigQuery (structured) Cloud Dataproc (Hadoop & Ecosystem) Cloud Bigtable (NoSQL HBase) Cassandra hBase MongoDBRabbit MQ Kafka Wave 2 Wave 3 Visualise Cloud DataLab (iPython/Jupyter) Data Studio 360 Tableau Qlik
- 8. 8Data & Analytics Exploration & Collaboration Databases Storage Data Preparation & Processing Analytics Advanced Analytics & Intelligence Google Cloud Data Platform Mobile apps Sensors and devices Web apps Relational Key-value Document SQL Wide Column Object Stream processing Batch processing Data preparation Federated query Data catalog Data exploration Data visualization Developers Data scientists Business analysts Development environment for Machine Learning Pre-Trained Machine Learning models Data Ingestion Messaging Logs
- 9. 9Data & Analytics Data Preparation & Processing Cloud Dataflow Cloud Dataproc Exploration & Collaboration Google BigQuery Cloud Datalab Google Analytics 360 Cloud Dataproc Google Cloud Data Platform Mobile apps Sensors and devices Web apps Developers Data scientists Business analysts Data Ingestion Cloud Pub/Sub App Engine Databases/ Storage Cloud SQL Cloud Bigtable Cloud Datastore Cloud Storage Analytics Google BigQuery Google Analytics 360 Cloud Dataproc Google Drive Advanced Analytics & Intelligence Cloud Machine Learning Translate API Vision API Speech API
- 10. 10Data & Analytics Managed Data Services - Focus on Insight vs Infrastructure PB+ Scale, No-Ops, Batch & Streaming of Data Insights/ Programming Resource Provisioning Performance Tuning Monitoring Reliability Deployment & Configuration Handling Growing Scale Utilization Improvements Insights/ Programming
- 11. proprietary & confidential | not for distribution "We are very excited about the productivity benefits offered by Cloud Dataflow and Cloud Pub/Sub. It took half a day to rewrite something that had previously taken over six months to build using Spark" Paul Clarke, Director of Technology, Ocado http://googlecloudplatform.blogspot.co.uk/2015/08/Announcing-General-Availability-of-Google-Cloud-Dataflow-and-Cloud-Pub-Sub.html
- 12. Hadoop + Local SSD 5X the IOPS at 0.5 the cost of AWS local SSD Up to 1.5TB per instance 680,000 read IOPS and < 1ms latency1 2 3
- 13. 13Data & Analytics – Mattias P Johansson, Software Engineer, Spotify “With Google Cloud Platform, we benefitted by having a virtual supercomputer on demand, without having to deal with all the usual space, power, cooling and networking issues. Just a few years ago, we would have needed to use the largest supercomputers on the planet to do what we’re now able to do with Google” – Mark Johnson, CEO, Descartes Labs “Right at the start of the partnership we were able to reduce time to insight from 96 hours to 30 minutes by using BigQuery.” – Gary Sanders, Head of Digital Analytics, Lloyds Banking Group “Everyone involved unanimously picked GCP. It came down to this: we believe the core technology is better.” – Peter Bakkam, Platform Lead, Quizlet Do you feel this way about your Data Warehouse?
- 14. 14Data & Analytics Data Warehouses/Lakes Machine Intelligence Data Warehouse is the foundation of something bigger Predictive + Prescriptive analytics = Advanced analytics Cloud On Premises Machine Learning APIs Train your own Models
- 15. 15Data & Analytics Automatically categorize, and automatically extract value Evaluate the model by applying it against additional manually categorized data, correct and tune Machine intelligence is already making a huge difference and there are many, many more opportunities Capture lots of examples of correct evaluations for that categorization, and use them to train an ML model Identify categorizations that provide value, categories you’re already evaluating for by hand today 1 2 3 4
- 16. 16| THE LEADERS CIRCLE Rapidly accelerating use of deep learning at Google AlphaGo Android Apps Gmail Maps Photos Robotics Speech Search Translation YouTube and many others ... Used across areas: 2012 2013 2014 2015 1500 1000 500 0 Number of directories containing model description files
- 17. 171717 BETAGA BETA Cloud Translate API Cloud Vision API Cloud Speech API Natural Language API GA Ready to use Machine Learning models
- 18. 18Data & Analytics Machine learning will drive every successful huge IPO win in the next 5 years.“ ”Eric Schmidt Executive Chairman, Alphabet Inc
- 19. 19Data & Analytics Now Stores and Analyzes Next Understands
- 20. 20Data & Analytics 20 Thanks!
Notas del editor
- ML + Google = :-) Mission is to organize the world’s information Information = data, data = oxygen Use of data can determine success
- a lot of info in pictures 23 billion words in Wikipedia 40 billion textual lines in StreetView Make the point that we didn’t realize how valuable the pictures were originally and later we revisited and extracted all this additional value. Willing to make a bet all the audience have similarly valuable data.
- Data access to a variety of data sources. The gartner advanced analytics customer reference survey indicates that while the majority of users are analyzing transactional data, new data sources — such as text, log and sensor data, and location data — are becoming increasingly common. Data preparation, exploration and visualization is a key area of functionality as analysis is performed by users who may lack familiarity with the data and have increasingly high expectations of tools for automating data discovery, visualization and preparation. The ability to develop and build analytic models, including clustering, classification and predictive models, forecasting models, simulation models and optimization models. Ability to deploy models and integrate them into business processes and applications. Deployment is a significant pain point for many organizations, so allowing easy adoption of models as part of a business process or application — rather than them just being exported as code or a database score — improves project success rates. Capabilities to perform platform, project and model management. The need to be able to validate the performance of models and track them once deployed is necessary; the ability to reuse models and audit their development and usage can be mandatory, rather than just desired, in certain more regulated industries and environments. High performance and scalability for both development and deployment. The ability to perform at high levels of speed and accuracy with large volumes data and streaming data is still critical for organizations, and with rising data volumes becomes even more of a differentiator.
- Speaker: Right now, Big Data = Big problems 1 - Removing the complexity of building and maintaining a Big Data system: Unlike with other Cloud services, Google provides the industry’s only NoOps Big Data platform. NoOps means that application developers will never have to speak with an operations professional again. NoOps will achieve this nirvana, by using cloud infrastructure-as-a-service to get the resources they need when they need them. 2 - Capture and store all data for all business functions: Developers can capture data using Pub/Sub or porting data from other Google services (i.e. Google Analytics). In addition, Google Cloud Storage offers developers durable and highly available object storage. Google created three simple storage product options to help developers improve the performance of their applications while keeping their costs low. These three product options use the same API, providing a simple and consistent method of access. 3 - Continuously accommodating greater data volumes and new data sources: Google understands that the amount of data companies have to store and analyze is growing exponentially. This is why we’re constantly innovating in order to offer cheaper and faster storage services (Nearline) but also making analysis tool such as BigQuery faster. 4 - Finding value in existing data very easily: Google BigQuery is designed to make it easy to analyze large amounts of data quickly. BigQuery enables analysts and developers to run fast SQL-like join and aggregate queries on datasets without the need for batch-based processing. 5 - Reducing the time from data collection to action: Google Cloud Platform for Big Data offers a proven and integrated end to end solution to make sense of large amounts of information in a very short amount of time. The end to end process of data management happens in the following stages: Capture data using Pub/Sub, porting data from other Google services i.e. Google Analytics Process data using DataFlow, 3rd party offerings i.e. Hadoop Store data using Google Cloud storage, Standard, DRA and Nearline or Bigtable and BigQuery Analyze data using BigQuery, 3rd party offerings i.e. Spark 6 - Removing the hurdles to innovate and iterate with Big Data: Google has led the industry with innovations in software infrastructure such as MapReduce, BigTable and Dremel. Today, Google is pushing the next generation of innovation with products such as Spanner and Flume. When you build on Cloud Platform, you get access to Google’s technology innovations faster.
- By 2020, predictive and prescriptive analytics will attract 40% of enterprises' net new investment in business intelligence and analytics. By 2018, more than half of large organizations globally will compete using advanced analytics and proprietary algorithms, causing the disruption of entire industries. advanced analytics is the analysis of all kinds of data using sophisticated quantitative methods (such as statistics, descriptive and predictive data mining, machine learning, simulation and optimization) to produce insights that traditional approaches to business intelligence (BI) — such as query and reporting — are unlikely to discover.
- Understanding that the space is categorizable, testable. Caetgorically. Do you have sources of data where you have correctly categorized already. Humans have interpreted data and put it into categories. Might Computationally/operationally expensive way to do this...how little data do I need. Test and tune. Process/app can be used to collect Automatic clustering/ Kmeans- Are there things grouped in there, categories I can’t see. Some groupings will seem irrelevant artificial-est Google Products: AlphaGo, Apps, Maps, Photos, Gmail, Speech, Android, YouTube, Translation, Robotics Research, Image Understanding, Natural Language Understanding, Drug Discovery Outside of Google, most popular use case are:
- To summarize, Cloud Machine Learning provides the latest innovations in vision and speech from Google Research and services like Photos, Google app, Translate, and Inbox. These ML driven capabilities are now simple APIs in Translate API, Vision API, and Speech API. Translate and Vision are fully launched. Cloud Speech and NL entered Beta. We’re very excited to bring this innovative technology to you guys
- Thanks so much!