Cloud Pak for Data Community

77 Entries
 
 
5 days ago

The concept of Knowledge Graph exists for long time. In recent time Google has made a significant paradigm shift in their search engine by introducing similar perception. In a nutshell, knowledge graph is a collecting information about different entities and their relationship to one another.


#Highlights-home
#Highlights
#Medium

Attachments
5 days ago

In my last blog post, I covered how you can deliver an AI pilot in just eight weeks and at the same time design your program in a way to scale the AI across your enterprise. Culture, architecture, and technology are fundamental to move from AI pilot to AI @ Scale.


#Highlights-home
#Highlights
#Medium

Attachments
5 days ago

Cloud Computing (CC) is becoming one of the most popular technology available today. One can define CC as a virtualization model that can use by a data centers to share their software and hardware resources. Multitenancy is a popular CC characteristic that allows data centers to achieve the cloud virtualization.


#Highlights
#Highlights-home
#Medium

Attachments
5 days ago

IBM Cloud Pak for Data (CPD) is an integrated data and AI platform that modernizes how businesses collect, organize and analyze data and infuse AI throughout their organizations. It integrates Watson AI technology with Data Management Platform, data ops, and governance and business analytics technologies.


#Highlights
#Highlights-home
#Medium

Attachments
5 days ago

Do you have time critical data flowing through your business? Are you looking to infuse AI into your applications to provide continuous intelligence? If so, IBM Streams can help you meet your time sensitive business problems. IBM Streams enables continuous and rapid analysis of massive volumes of data.


#Highlights
#Highlights-home
#Medium

Attachments
10 days ago

IBM Cloud Pak for Data 2.5 is available now and introduces some major changes.  Check out the What's new video.


#Spotlight

Attachments
10 days ago

Business Value and Use Case

The system provides to build capabilities for in-depth analysis, ratings, reach and ROI for Agencies, Brand Managers, MSOs and Broadcasters to identify and define the target audience and improve the campaign performance.

Objective

Identify the target audience for TV Advertising

Viewership ratings of programs and Ads for dissected target audience

Quantify the impact of TV and digital exposure on consumer behavior

Cross platform measurement of Media consumption to determine Reach, Frequency and key performance indicators

Insights

 



Audience Intelligence

  • Aggregated viewership measures, reach, historical trends & projections for program and commercial ratings
  • Integrate with TP and FP audience intelligence and behavioral data
  • Profile and segment households into demographic, interest-based, retail and lifestyle categories

 

Audience Targeting

  • Identify households that meet the target criteria
  • Enable geographic, demographic & behavioural addressability of TV advertising
  • Reduce wasted impressions to lower the cost of impact

 

Spot Recommendations

  • Generate spot recommendations against qualified potential customers using predictive analytics

 

Viewership Forecasting

 

  • Forecast future viewership using regression techniques
  • Project forecasting ratings to universal audience

 

 

Typical Metrics & reports

 

Program Rating

-          Audience that viewed the program as a percent of the audience population. Could be against the target audience alone, or complete audience.

Commercial Rating

-          Audience that viewed the commercial as a percent of the audience population. Could be against the target audience alone, or complete audience

Ad Impressions

-          Total number of times the advertisement was watched by target audience

Reach

-          Unique audience that viewed the program or commercial

Conversion

-          Unique audience that took a desired action after watching the commercials as a percentage of reach

Engagement

-          Total number of times the audience actively interacted with the advertisement for e.g. selecting the advertisement or watch the clip

Prerequisites of Cloud Pak for Data Services 

The accelerator requires Analytics Dashboard service.

Enablement Material

- Download the presentation by clicking here - Tavant Media Accelerators

Import the Accelerator

  1. Download the Media - TV Viewership.tar.gz file, which is available on the Git repository here -  https://github.com/tavant-media/ibm-cpd-accelerator
  2. Create the project by importing the ZIP file. For details, see Creating a project in the Cloud Pak for Data documentation.

Next Steps

Whom to contact. 

Ravi Peravali | ravi.peravali@tavant.com

T:408-519-5367| C: 510-673-4668 | F:408 519 5401

Tavant Technologies | www.tavant.com

3965 Freedom Circle, Suite #750, Santa Clara, CA 95054

Abhay Dubey | abhay.dubey@tavant.com

T: 408-654-5205| C: 408-507-9136

Tavant Technologies | www.tavant.com

3965 Freedom Circle, Suite 750, Santa Clara, CA 95054, USA

About Tavant

Tavant

Tavant is a digital products and solutions company that provides impactful results to its customers across a wide range of industries such as Consumer Lending, Aftermarket, Media & Entertainment, and Retail in North America, Europe, and Asia-Pacific. Our solutions, powered by Artificial Intelligence and Machine Learning algorithms, help improve operational efficiency, productivity, speed and accuracy in the interconnected world to succeed in a rapidly changing business environment.

Founded in 2000, headquartered in Santa Clara, the company employs over 2500 people and is a recognized top employer. Tavant is an ISO 27001 compliant and SEI-CMMI level 4 compliant organization.

 

Media

Tavant partners with leading media & entertainment companies to develop innovative engineering solutions to deliver and monetize content across screens and devices. Tavant’s engineering services, coupled with extensible frameworks, enable media companies to gain better insights, make smarter business decisions, deliver exceptional customer experiences, and maximize revenues across platforms. Areas of expertise include OTT/broadcast operations, advanced advertising, and AI-driven data analytics. Founded in 2000, and headquartered in Santa Clara, CA, Tavant’s team of over 2,500 engineers provides impactful results to its clients across North America, Europe, and Asia-Pacific. For more information, visit www.tavant.com/media


#Featured-area-2
#Featured-area-2-home

Attachments
26 days ago

Business Value and Use Case

The Financial industry has been exploring the use of AI and machine learning in their use-cases, but given the monetary risk associated with the transactions and implementation, they post resistance. Traditional algorithmic trading has evolved in recent years and now high-computational systems automates the tasks, but traders still build the policies that govern choices to buy and sell. An algorithmic model for buying stocks based on a list of valuation and growth metric conditions might define a “buy” or “sell” signal that would in turn be triggered by some specific rules that the trader has defined. Compared to traditional machine learning algorithms, using reinforcement learning makes the entire machine trading a fully automated method, as the policy building is now done by the model and not the traders. Using RL, the reward is hypothesized to be better than the policies manually built by traders, as hundreds of signals can be much efficiently analyzed by algorithms.

Assets Included

https://github.ibm.com/DSE-ART/Incubator/tree/master/ReinforcementLearning

Prerequisites of Cloud Pak for Data Services 

- NA

Enablement Material

- https://ibm.box.com/s/mhgbyz86ahocub6d2i8nuhl4gz896lxo

Import the Accelerator

- Go to scripts/Final_Agent to see the RL model.
- Go to scripts/Final_Functions to see all the utility function for the accelerator.
- Go to scripts/Final_Train to train the RL model.
- Go to scripts/Final_Evaluate to see the code used to evaluate the RL model on the new stock price data

Next Steps

Aishwarya Srinivasan (aishwarya.srinivasan@ibm.com)

Release Notes

- information of version the accelerator has been tested with.

About the Developer

Licensing

Importing the accelerator

Attachments
one month ago

IBM Cloud Pak for Data version 2.5.0 contains many new features and enhancements. See the What's new topic for details.
Now, you can migrate your data from IBM InfoSphere Information Server 11.7.1 and use these features.

To migrate your data, you must first install the Watson Knowledge Catalog service patch 3.0.0.2. See the detailed instructions in the 'Installing Watson Knowledge Catalog service patch 3.0.0.2' PDF document.

After you install the patch, follow the migration procedure that is provided in the 'Migrating IBM Information Server 11.7.1 data to IBM Cloud Pak for Data 2.5.0' PDF document.

Note: This migration patch is the first in the series of migration patches. It contains limitations. The details about what data cannot be migrated is listed in the migration document.

Attachments