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Meta-Learning | @CloudExpo @ProgressSW #BigData #AI #ML #DX #Analytics

There are many solutions that claim to democratize analytics, but they are really constrained

True Democratization of Analytics with Meta-Learning
By Mark Troester

There are many solutions that claim to democratize analytics, but they are really constrained. A meta-learning approach democratizes without limits.

The democratization of analytics has become a popular term, and a quick Google search will generate results that explore the necessity of empowering more people with analytics and the rise of citizen data scientists. The ability to easily make better use of your (constantly growing) pool of data is a critical driver of business success, but many of the existing solutions that claim to democratize analytics only do so within severe limits. If you have a complex business scenario and are looking to get revolutionary insights using them, it’s easy to come away disappointed.

However, the democratization of analytics isn’t just a buzzword that refers to a narrow approach. It’s possible to do so much more. Let’s quickly review the current state of the market that you’re likely familiar with, and then dive into our proposed solution.

Lightweight Solutions that Oversimplify
One way this type of solution is marketed is as something that’s simple because it works in an environment business leaders are already familiar with, like Excel or Tableau. These solutions tend to be lightweight and are really about easily generating a digestible report. That’s all well and good, but it’s really democratizing report generation and lightweight analysis rather than enabling you to develop truly predictive scenarios that require Machine Learning.

Narrowly Defined Analytics as a Service
Another option that is gaining adoption is to use pre-trained models usable out-of-the-box for image analysis and classification, speech to text conversion, and translation services. While these make certain limited use cases available to more organizations, they don’t actually democratize the predictive analytics processing related to business specific time-series scenarios.

Cloud Environments that are only a Framework
Finally, there are numerous cloud vendors that take care of managing the infrastructure necessary for Big Data analytics and Machine Learning, whether it’s hosting Hadoop/MapReduce, Spark, etc., providing managed database support, or hosting machine intelligence software libraries like TensorFlow. At the end of the day, these options are really democratizing the infrastructure necessary to support Machine Learning—they aren’t democratizing the Data Scientist lifecycle itself, something we discuss in detail a little later in the post.

But What about More Sophisticated Business Scenarios?
The solutions above may technically “democratize” some form of analytics, but they fall short in democratizing Machine Learning for individual business use cases like predictive maintenance for the Industrial IoT, improving patient outcomes in healthcare, detecting fraud in financial services, etc. So while simple scenarios are becoming a commodity, business scenarios that provide the most value are beyond the reach of most organizations.

Why?

Because the Machine Learning or Data Scientist lifecycle is complex. A successful implementation includes a business requirements phase, data preparation, data modeling, and production deployment work. The last three phases are particularly resource intensive.

  • The data preparation phase involves collecting the data, cleansing the data, and transforming the data—and multiple sets of data are required for scoring and testing.
  • The data modeling phase is especially demanding and involves feature engineering, algorithm selection, testing, tuning and model optimization. These steps need to be repeated until the models reach an acceptable level of quality.
  • Then there is the deployment—you have to take the models and deploy them in production using operational data. The work doesn’t end there, as you must continuously review and revise the models to keep up with changes in the environment.

It’s pretty clear that this is a completely different challenge that the options described above can’t address. While there are cloud options that will manage the infrastructure, and there are tools that make the data scientist more efficient, there is a dearth of solutions that tackle the democratization of complex Machine Learning.

The need for democratization is driven by the amount of time and resources it takes to do this manually—even with a team of data scientists. And for those that don’t have data scientists, this is a non-starter given traditional tools and solutions.

Enter Machine Learning and Meta-Learning
It’s evident that there is a need for a better way forward when it comes to solving these complex business challenges. Data scientists have to be freed from the laborious day to day grind that consumes so much of their time today, enabling them to more effectively support a higher number of business scenarios in less time.

Progress DataRPM is designed specifically to meet this need. By developing an innovative machine automated approach, we are able to automate a range of complex tasks that the other solutions above simply can’t.

  • DataRPM uses a meta-data approach to remember, share and apply learnings from the model experiments. This approach speeds the iterative process required to build and test models, and has also proven to increase the accuracy of production analytic results tremendously.
  • DataRPM also leverages a novel approach for detecting failures. Traditional methods limit the analytics approach to building models that identify future failure or require optimization strictly based on past failures, but this approach provides poor coverage given that it can’t predict random failures (which are the predominant type of failure). DataRPM instead models normal behavior and then detects deviations from normal. These are flagged as potential problems that can be managed effectively by the business. Next, this intelligence is then fed back into the model so that it is continuously improved based on production data.

This solution allows your team to focus the most strategic and actionable part of the process, which is analyzing and assessing the results. Whether you currently employ data scientists or not, it reduces the amount of time you need to allocate to evaluating and creating complex models.

Rather than constrain analytics and generate a simple or limited result, the meta-learning approach looks fully at the unique problems facing your business, is flexible enough to be adapted to new problems as they arise and is constantly improving. By automating some of the most arduous components of data analysis, you’re free to focus on delivering the insights and outcomes you need—quickly. It's all part of our cognitive-first vision for business applications. You can learn more about our platform for cognitive predictive maintenance here.

Internet of @ThingsExpo, taking place October 31 - November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with 21st Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world.

Download Show Prospectus ▸ Here

The Internet of Things (IoT) is the most profound change in personal and enterprise IT since the creation of the Worldwide Web more than 20 years ago.

All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades.

With major technology companies and startups seriously embracing IoT strategies, now is the perfect time to attend @ThingsExpo in Silicon Valley. Learn what is going on, contribute to the discussions, and ensure that your enterprise is as "IoT-Ready" as it can be!

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.

Track 1. Enterprise Cloud | Cloud-Native
Track 2.
Big Data | Analytics
Track 3. Internet of Things | IIoT | Smart Cities

Track 4. DevOps | Digital Transformation (DX)

Track 5. APIs | Cloud Security | Mobility

Track 6.
AI | ML | DL | Cognitive
Track 7.
Containers | Microservices | Serverless
Track 8. FinTech | InsurTech | Token Economy

Cloud Expo | @ThingsExpo 2017 Silicon Valley
(October 31 - November 2, 2017, Santa Clara Convention Center, CA)

Cloud Expo | @ThingsExpo 2018 New York 
(June 12-14, 2018, Javits Center, Manhattan)

Download Show Prospectus ▸ Here

Every Global 2000 enterprise in the world is now integrating cloud computing in some form into its IT development and operations. Midsize and small businesses are also migrating to the cloud in increasing numbers.  

Companies are each developing their unique mix of cloud technologies and services, forming multi-cloud and hybrid cloud architectures and deployments across all major industries. Cloud-driven thinking has become the norm in financial services, manufacturing, telco, healthcare, transportation, energy, media, entertainment, retail and other consumer industries, and the public sector.

Cloud Expo is the single show where technology buyers and vendors can meet to experience and discus cloud computing and all that it entails. Sponsors of Cloud Expo will benefit from unmatched branding, profile building and lead generation opportunities through:

  • Featured on-site presentation and ongoing on-demand webcast exposure to a captive audience of industry decision-makers.
  • Showcase exhibition during our new extended dedicated expo hours
  • Breakout Session Priority scheduling for Sponsors that have been guaranteed a 35-minute technical session
  • Online advertising in SYS-CON's i-Technology Publications
  • Capitalize on our Comprehensive Marketing efforts leading up to the show with print mailings, e-newsletters and extensive online media coverage.
  • Unprecedented PR Coverage: Editorial Coverage on Cloud Computing Journal.
  • Tweetup to over 75,000 plus followers
  • Press releases sent on major wire services to over 500 industry analysts.

For more information on sponsorship, exhibit, and keynote opportunities, contact Carmen Gonzalez by email at events (at) sys-con.com, or by phone 201 802-3021.

The World's Largest "Cloud Digital Transformation" Event

@CloudExpo | @ThingsExpo 2017 Silicon Valley
(Oct. 31 - Nov. 2, 2017, Santa Clara Convention Center, CA)

@CloudExpo | @ThingsExpo 2018 New York 
(June 12-14, 2018, Javits Center, Manhattan)

Full Conference Registration Gold Pass and Exhibit Hall ▸ Here

Register For @CloudExpo ▸ Here via EventBrite

Register For @ThingsExpo ▸ Here via EventBrite

Register For @DevOpsSummit ▸ Here via EventBrite

Sponsorship Opportunities

Sponsors of Cloud Expo | @ThingsExpo will benefit from unmatched branding, profile building and lead generation opportunities through:

  • Featured on-site presentation and ongoing on-demand webcast exposure to a captive audience of industry decision-makers
  • Showcase exhibition during our new extended dedicated expo hours
  • Breakout Session Priority scheduling for Sponsors that have been guaranteed a 35 minute technical session
  • Online targeted advertising in SYS-CON's i-Technology Publications
  • Capitalize on our Comprehensive Marketing efforts leading up to the show with print mailings, e-newsletters and extensive online media coverage
  • Unprecedented Marketing Coverage: Editorial Coverage on ITweetup to over 100,000 plus followers, press releases sent on major wire services to over 500 industry analysts

For more information on sponsorship, exhibit, and keynote opportunities, contact Carmen Gonzalez (@GonzalezCarmen) today by email at events (at) sys-con.com, or by phone 201 802-3021.

Secrets of Sponsors and Exhibitors ▸ Here
Secrets of Cloud Expo Speakers ▸ Here

All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades.

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo@ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.

Delegates to Cloud Expo | @ThingsExpo will be able to attend 8 simultaneous, information-packed education tracks.

There are over 120 breakout sessions in all, with Keynotes, General Sessions, and Power Panels adding to three days of incredibly rich presentations and content.

Join Cloud Expo | @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, for three days of intense Enterprise Cloud and 'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets.

Financial Technology - or FinTech - Is Now Part of the @CloudExpo Program!

Accordingly, attendees at the upcoming 21st Cloud Expo | @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.

Financial enterprises in New York City, London, Singapore, and other world financial capitals are embracing a new generation of smart, automated FinTech that eliminates many cumbersome, slow, and expensive intermediate processes from their businesses.

FinTech brings efficiency as well as the ability to deliver new services and a much improved customer experience throughout the global financial services industry. FinTech is a natural fit with cloud computing, as new services are quickly developed, deployed, and scaled on public, private, and hybrid clouds.

More than US$20 billion in venture capital is being invested in FinTech this year. @CloudExpo is pleased to bring you the latest FinTech developments as an integral part of our program, starting at the 21st International Cloud Expo October 31 - November 2, 2017 in Silicon Valley, and June 12-14, 2018, in New York City.

@CloudExpo is accepting submissions for this new track, so please visit www.CloudComputingExpo.com for the latest information.

Speaking Opportunities

The upcoming 21st International @CloudExpo@ThingsExpo, October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY announces that its Call For Papers for speaking opportunities is open.

Submit your speaking proposal today! ▸ Here

About SYS-CON Media & Events
SYS-CON Media (www.sys-con.com) has since 1994 been connecting technology companies and customers through a comprehensive content stream - featuring over forty focused subject areas, from Cloud Computing to Web Security - interwoven with market-leading full-scale conferences produced by SYS-CON Events. The company's internationally recognized brands include among others Cloud Expo® (@CloudExpo), Big Data Expo® (@BigDataExpo), DevOps Summit (@DevOpsSummit), @ThingsExpo® (@ThingsExpo), Containers Expo (@ContainersExpo) and Microservices Expo (@MicroservicesE).

Cloud Expo®, Big Data Expo® and @ThingsExpo® are registered trademarks of Cloud Expo, Inc., a SYS-CON Events company.

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Progress offers the leading platform for developing and deploying mission-critical, cognitive-first business applications powered by machine learning and predictive analytics.

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