Comments
yourfanat wrote: I am using another tool for Oracle developers - dbForge Studio for Oracle. This IDE has lots of usefull features, among them: oracle designer, code competion and formatter, query builder, debugger, profiler, erxport/import, reports and many others. The latest version supports Oracle 12C. More information here.
Cloud Computing
Conference & Expo
November 2-4, 2009 NYC
Register Today and SAVE !..

2008 West
DIAMOND SPONSOR:
Data Direct
SOA, WOA and Cloud Computing: The New Frontier for Data Services
PLATINUM SPONSORS:
Red Hat
The Opening of Virtualization
GOLD SPONSORS:
Appsense
User Environment Management – The Third Layer of the Desktop
Cordys
Cloud Computing for Business Agility
EMC
CMIS: A Multi-Vendor Proposal for a Service-Based Content Management Interoperability Standard
Freedom OSS
Practical SOA” Max Yankelevich
Intel
Architecting an Enterprise Service Router (ESR) – A Cost-Effective Way to Scale SOA Across the Enterprise
Sensedia
Return on Assests: Bringing Visibility to your SOA Strategy
Symantec
Managing Hybrid Endpoint Environments
VMWare
Game-Changing Technology for Enterprise Clouds and Applications
Click For 2008 West
Event Webcasts

2008 West
PLATINUM SPONSORS:
Appcelerator
Get ‘Rich’ Quick: Rapid Prototyping for RIA with ZERO Server Code
Keynote Systems
Designing for and Managing Performance in the New Frontier of Rich Internet Applications
GOLD SPONSORS:
ICEsoft
How Can AJAX Improve Homeland Security?
Isomorphic
Beyond Widgets: What a RIA Platform Should Offer
Oracle
REAs: Rich Enterprise Applications
Click For 2008 Event Webcasts
In many cases, the end of the year gives you time to step back and take stock of the last 12 months. This is when many of us take a hard look at what worked and what did not, complete performance reviews, and formulate plans for the coming year. For me, it is all of those things plus a time when I u...
SYS-CON.TV
From Science to Art: Making Machine Learning Approachable | @CloudExpo #AI #ML #Cloud
The high barrier to entry prevents many companies from tapping into the full potential of machine learning

From Science to Art: Making Machine Learning Approachable
By Sundeep Sanghavi

The high barrier to entry prevents many companies from tapping into the full potential of machine learning. But what if you could make it more accessible?

We’re in the midst of a data explosion, with today’s enterprises amassing goldmines of information (25 quintillion bytes of data every day, according to some reports). But what exactly are they doing with this data? Considering the volume of data being collected is quickly becoming unmanageable, now is a good time to shift from manual machine learning to a cognitive approach. This enables businesses to better capitalize on their data and facilitate agile decision-making.

At this point, much of the discussion around machine learning has pivoted from adoption to how to simplify the adoption and implementation process. Many enterprises are looking to answer the question of how you break down the immensely tall barriers around data science so you can fully tap into the undeniable advantages machine learning has to offer.

Today, many businesses are simply collecting data, with little being done to translate it into usable intelligence. The data and people wind up trapped in siloes, and beyond that, any attempts at data analytics so far have usually been done on a limited scale. Generally speaking, these efforts were done with either one tool or one team, resulting in a very localized perspective of a much larger context.

For instance, a dashboard of results contains minimal traces of where insights have been sourced from, and a data table generated during one phase of a process may not be usable for any processes further down the stream. What enterprises actually need is for all involved users to be able to access the required intelligence so the necessary parties can leverage this insight to drive business goals.

From Inscrutably Scientific to Unbelievably Intuitive
The demand for machine learning is growing faster than ever before, and it’s currently one of the fastest growing disciplines of data science. Unfortunately, the barriers to entry in terms of cost and skill requirements are still as daunting as ever. This has led to a data scientist arms race, with enterprises frantically competing to woo, hire and retain expensive data scientists and engineers with fancy degrees to stay one step ahead. In fact, the number of job openings for machine learning engineers and data scientists far exceeds the availability—especially with so many already snapped up by industry titans like Google, Facebook and IBM.

So, where can you find these reclusive coders? It’s an understatement to even say it’s not an easy task.

But what if we flipped that equation on its head? Imagine if machine learning was no longer restricted to the world of genius-level data scientists and engineers—instead, it was open-source software that enabled non-coders and non-technical staff to access, build and deploy machine learning capabilities.

This would enable businesses to widen the practical application of machine learning to a much higher degree, while also lowering cost barriers. Everyone from developers to operations managers to business analysts to even business stakeholders would be able to cash in on the benefits of machine learning.

You Don’t Need a PhD to Crack Machine Learning
We at the Progress DataRPM team believe that data science is not merely about the algorithms, it’s about the value that the algorithm generates. DataRPM democratizes machine learning and data science through an innovative platform that arms every employee in an organization—from frontline employees to the board—with seamless, complete intelligence. It also helps them leverage the power of cognitive analytics for existing business applications, while at the same time opening up opportunities for rapidly building cognitive applications.

With this degree of accessibility, machine learning could spread to millions, or possibly even billions, of people. This means that companies no longer have to expend precious time and resources on attracting and hiring entire teams of expensive data scientists to write code. With pre-populated algorithms, parameters and configurations, you’ll eliminate the need for manual data science coding altogether. The machines themselves will be able to build models and predict outcomes, leaving your team free to spend more time analyzing and implementing the results.

With the cognitive approach to machine learning, several models can be built simultaneously, so processes that were once linear can now happen in parallel. This will not only save precious time, but also empower enterprises to amplify the scope of data investments. Deep, meaningful insights are extracted from each model and built by abstracting the required code, eliminating the need for manual coding. Thus, businesses can leverage the benefits of predictive analytics and insights while also monetizing their big data investments for a fraction of the time and effort they would’ve normally spent.

Read the original blog entry...

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

SOA World Latest Stories
As DevOps methodologies expand their reach across the enterprise, organizations face the daunting challenge of adapting related cloud strategies to ensure optimal alignment, from managing complexity to ensuring proper governance. How can culture, automation, legacy apps and even budget...
You know you need the cloud, but you’re hesitant to simply dump everything at Amazon since you know that not all workloads are suitable for cloud. You know that you want the kind of ease of use and scalability that you get with public cloud, but your applications are architected in a w...
Is advanced scheduling in Kubernetes achievable?Yes, however, how do you properly accommodate every real-life scenario that a Kubernetes user might encounter? How do you leverage advanced scheduling techniques to shape and describe each scenario in easy-to-use rules and configurations?...
The cloud era has reached the stage where it is no longer a question of whether a company should migrate, but when. Enterprises have embraced the outsourcing of where their various applications are stored and who manages them, saving significant investment along the way. Plus, the clou...
While some developers care passionately about how data centers and clouds are architected, for most, it is only the end result that matters. To the majority of companies, technology exists to solve a business problem, and only delivers value when it is solving that problem. 2017 brings...
DevOps is under attack because developers don’t want to mess with infrastructure. They will happily own their code into production, but want to use platforms instead of raw automation. That’s changing the landscape that we understand as DevOps with both architecture concepts (CloudNati...
Subscribe to the World's Most Powerful Newsletters
Subscribe to Our Rss Feeds & Get Your SYS-CON News Live!
Click to Add our RSS Feeds to the Service of Your Choice:
Google Reader or Homepage Add to My Yahoo! Subscribe with Bloglines Subscribe in NewsGator Online
myFeedster Add to My AOL Subscribe in Rojo Add 'Hugg' to Newsburst from CNET News.com Kinja Digest View Additional SYS-CON Feeds
Publish Your Article! Please send it to editorial(at)sys-con.com!

Advertise on this site! Contact advertising(at)sys-con.com! 201 802-3021


SYS-CON Featured Whitepapers
ADS BY GOOGLE