Cloudera Fast Forward Labs Vision of Applied Machine Learning
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Learn how Cloudera provides a unified platform that breaks down data silos commonly seen in organizations. By unifying the data needed for applied machine learning, organizations are better equipped to gather valuable insights from their data.
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Cloudera Fast Forward Labs Vision of Applied Machine Learning
- 1. Cloudera Fast Forward Labs The Vision & Challenge of Applied Machine Learning Brian Goral | Business Manager
- 2. © Cloudera, Inc. All rights reserved. 2 Machine Learning, defined: Algorithms and methods that extract useful patterns from data.
- 3. © Cloudera, Inc. All rights reserved. 3 Big Data Analytics +-^x Data Science = ML ? AI
- 4. © Cloudera, Inc. All rights reserved. 4 “Artificial intelligence is whatever computers cannot do until they can.”
- 5. © Cloudera, Inc. All rights reserved. 5 Puppy or Bagel?
- 6. © Cloudera, Inc. All rights reserved. 6 Puppy, Muffin, or Fried Chicken?
- 7. © Cloudera, Inc. All rights reserved. 7
- 8. © Cloudera, Inc. All rights reserved. 8
- 9. © Cloudera, Inc. All rights reserved. 9
- 10. © Cloudera, Inc. All rights reserved. 10
- 11. © Cloudera, Inc. All rights reserved. 11 However…
- 12. © Cloudera, Inc. All rights reserved. 12
- 13. Design What should we build? Science Will it work? Data Data Data Data product lifecycle Engineering Will it scale? Performance Is it having an impact?
- 14. © Cloudera, Inc. All rights reserved. 14 (1) Uncertainty about the future (2) Machine learning silos (3) Collaboration and workflow issues Barriers to Transformation
- 15. © Cloudera, Inc. All rights reserved. 15
- 16. © Cloudera, Inc. All rights reserved. 16
- 17. © Cloudera, Inc. All rights reserved. 17
- 18. © Cloudera, Inc. All rights reserved. 18 (1) Uncertainty about the future (2) Machine learning silos (3) Collaboration and workflow issues Barriers to Transformation
- 19. © Cloudera, Inc. All rights reserved. 19 Typical pattern: machine learning “silos.” Fraud ClaimsUnderwriting MarketingClinical MEDI-SPAN
- 20. © Cloudera, Inc. All rights reserved. 20 ●Siloed teams complicate hiring, training, and work balance ●Siloed processes create complexity, overlap, and rework ●Siloed tools and data create multiple issues: • Synchronizing metrics • Difficult data access • Complex and challenging model deployment ●Machine learning silos markedly increase cost to ●license, provision, train, and maintain. ML silos impede transformation.
- 21. © Cloudera, Inc. All rights reserved. 21 Multi-Task Learning
- 22. © Cloudera, Inc. All rights reserved. 22 (1) Uncertainty about the future (2) Machine learning silos (3) Collaboration and workflow issues Barriers to Transformation
- 23. © Cloudera, Inc. All rights reserved. 23
- 24. © Cloudera, Inc. All rights reserved. 24
- 25. © Cloudera, Inc. All rights reserved. 25 • Business analyst: represents stakeholder, defines requirements • Data scientist: principal investigator, accountable for accuracy and validity • Peer data scientist: provides independent review of work • Machine learning specialists: expertise in subspecialties • Compliance specialists: verify that predictive model aligns with policy, regulations • Data engineer: develops production data pipelines • DevOps specialists: provide provisioning and support for data science platform • Data management specialists: provide expertise in data sources • Application developers: embed predictive models in end user applications • Systems administrators: support infrastructure for production application • Security specialists: evaluate application for security compliance How many people touch your data product?
- 26. © Cloudera, Inc. All rights reserved. 26 (1) Common unified platform for data science (2) Secure self-service access • Data • Compute • Storage (3) Support for the open source ecosystem (4) Tools that simplify collaboration (5) Support for the complete data science pipeline What do data science teams need?
- 27. © Cloudera, Inc. All rights reserved. 27 Big Data Analytics +-^x Data Science = ML ? AI
- 28. The general formulation of an algorithm ≠ the solution to your problem.
- 29. © Cloudera, Inc. All rights reserved. 29 Cloudera Fast Forward Labs
- 30. © Cloudera, Inc. All rights reserved. 30 Research & Advising Stay on top of emerging ML technologies Strategic Engagements Define data strategy Advise on implementation Application Development Track Feasibility studies: Build a ML product with you How Does Fast Forward Labs Engage?
- 37. © Cloudera, Inc. All rights reserved. 37 Provides clarity about Where & How to invest in ML/AI • Team/Infrastructure/Commercial Tools • Resource maximization • Resources for DS maintenance and growth Helps you deliver early value from ML/AI investments • Practical guidance on use of specific algorithms/techniques • Avoid technical dead ends • Expedite development • Communication with leadership on technical challenges Cloudera Fast Forward Labs
- 38. © Cloudera, Inc. All rights reserved. 38 We bring proprietary IP from our research process and our building of new applied data capabilities. We bring knowledge learned from our advising work across industries on valuable problems to solve. We use this to identify and execute on new product opportunities. Cloudera Fast Forward Labs
- 39. © Cloudera, Inc. All rights reserved. 39 Cloudera is a unified platform for data and machine learning.
- 40. © Cloudera, Inc. All rights reserved. 40 Cloudera Enterprise 46 The modern platform for machine learning and analytics optimized for the cloud EXTENSIBLE SERVICES CORE SERVICES DATA ENGINEERING OPERATIONAL DATABASE ANALYTIC DATABASE DATA CATALOG INGEST & REPLICATION SECURITY GOVERNANCE WORKLOAD MANAGEMENT DATA SCIENCE S3 ADLS HDFS KUDU STORAGE SERVICES
- 41. © Cloudera, Inc. All rights reserved. 41 Cloudera Fast Forward Labs • Helps you build strategy • Focus resources on specific effective techniques Cloudera Enterprise Platform • Unified platform • For data and machine learning Cloudera Data Science Workbench • Modern collaborative platform • Self-service access to data, storage and compute • Python, R, Spark, and Deep Learning ecosystems Cloudera: Data Science and Machine Learning For the Enterprise.
- 42. © Cloudera, Inc. All rights reserved. Thank you 42 Brian Goral - Cloudera Fast Forward Labs @brian_goral, @cloudera, @fastforwardlabs
Notes de l'éditeur
- Thank you, Joao. Our next speaker comes to us all the way from Brooklyn, New York, where the Fast Forward Labs team is headquartered. Cloudera acquired Fast Forward Labs last year to advance machine learning in the enterprise. The FFL team has a clear vision of the future and a deep expertise in applying machine learning and AI to practical business problems. Brian Goral guides large client programs and provides operations direction for Cloudera's Fast Forward Labs team — when he’s available at the FFL offices in Brooklyn he joins the research and advising teams, bringing emerging machine learning concepts to life in client business use cases. Prior to joining the Fast Forward Labs team Brian lived a 15-plus year career focused on data collection systems and application of global data to decision-making and policy-setting. An alumnus of Michigan State University with a Masters from the University of North Carolina, Brian hails from Milwaukee though now hangs his hat in New York City. Without further adieu, please welcome Brian Goral.
- Zebra Medical Vision, an Israeli startup, uses deep learning to diagnose diseases of the bone system, liver, lungs, cardiovascular system, and the brain.
- Hospitals have used machine learning to predict rehospitalization for years. With new techniques, they are able to markedly improve the predictive power of readmission models. -- Mining text from provider notes and other documents in the EHR system -- Building models specific to individual diseases and diagnoses CHS transformed readmissions modeling into priority scores available to care managers. in a year and a half to a two-year period Carolinas Health System was able to drop the readmission rate from 21 percent to 14 percent.
- For the insurance industry, researchers at Purdue University developed a system that uses machine learning to assess disaster damage. This makes it possible for insurers to rapidly predict claims, and serves as an independent check on human assessors.
- Lloyds Banking Group uses deep learning to develop unique identifiers for each customer’s voice. The bank uses these voice profiles to confirm the identity of people who contact the call center, reducing fraud and improving operations.
- Many organizations STRUGGLE to profit from machine learning
- It’s difficult because it’s often hard to ask the right questions, difficult because it’s not straightforward programming - it really is science and experimentation, difficult because of the rapidly growing volume, difficult because the metrics are generally measured somewhere relative to chance which can be difficult to communicate - when a data set tells you you have a 95% certainty in a particular outcome that’s one thing, but what about when the data only supports a solution with a 65% accuracy and you need to dig deeper. Executives in a lot of organizations are not used to being given that kind of response. You also know, better than most organizations, that a company, one of your clients, can’t outsource understanding of your own workplace or necessarily outsource their internal data product development without risking poor integration And with so much going on under the umbrella of Artificial Intelligence these days Executives need trusted advisors to navigate a fast-moving landscape of machine learning or “AI” as many people term it. Data products underpin much of the current business and government decision-making occurring today. Data - machine learning in particular is a huge - but difficult to execute on - opportunity for every organization.
- Data products themselves are difficult at the tactical level but there are also these strategic barriers to transformation.
- Nobody knows what to buy from whom
- Skills gap
- The technology is moving so fast…
- Departmental purchases Different ML tools for different ML use cases Absence of common best practices and standards Provisioning is all over the map One team might use Databricks another uses a competing black box software you’ll never get your data back from.
- It’s important to consolidate It saves money and process and smooths interactions - and with ML in particular there’s an added benefit - multi-task learning.
- Multi-task learning is building a ML model on differently trained tasks to the benefit/enhancement of each. In the health care world this might look something like training separate models on claims data and patient care data and finding new cost savings insights in each when the commonalities and differences in the models are combined and exploited
- We hear a lot about data science “heroes” -- genius individual contributors who, singlehandedly, produce brilliant insights that change lives,
- In fact, successful data science requires a collaborative approach from many contributors
- Just saying something is a deep learning problem is like saying you’re going to deliver a vehicle with a 12-cylinder engine. Each of these vehicles has a 12-cylinder engine, but there are a lot of differences in producing them.
- We’re there because academic research is doing some amazing work, but they’re not trying to solve your applied problem. The general formulation of an algorithm does not equal your use case solution. I can pull up some very cool use cases of deep learning using Cloudera in areas like disaster recovery, cardiac monitoring in health care, and voice identification in banking - but there is a big difference between saying that you have a deep learning challenge and bringing you to a solution. We’re here to help bridge that gap.
- But obviously it takes more than good people and processes. You need the right technology. Let’s get down to brass tacks on what the software is about We’re based on an open source core. A complete, integrated enterprise platform leveraging open source HOSS business model - core set of platform capabilities – we contribute actively into that community. and we layer value added software on top - that’s how we run our business. But what’s truly differentiating about our platform is the enterprise experience you get. It’s why we’re able to claim 7 of the top ten banks and 9 of the top ten telcos are our customers. For regulated industries, the enterprise experience is critical. Multi-cloud – No vendor lock in. Work in the environment of your choice. Better pricing leverage Managed TCO – Multiple pricing and deployment options Integrated – Integrated components with shared metadata, security and operations Secure - Protect sensitive data from unauthorized access – encryption, key management Compliance – Full auditing and visibility Governance – Ensure data veracity