Apps Run The World Apps Purchases

Customer	Industry	Empl.	Revenue	Country	Vendor	Application	Category	When	VAR/SI	Insight Source
Astute Business Solutions	Professional Services	150	$20M	United States	Oracle	Oracle Management Cloud	ML and Data Science Platforms	2017	n/a	In 2017, Astute Business Solutions implemented Oracle Management Cloud, selecting Oracle Management Cloud within the ML and Data Science Platforms category to introduce autonomous monitoring and management for IT operations. The deployment used Oracle Management Cloud as a cloud hosted platform to apply machine learning for anomaly detection and automation of routine operational tasks across the IT environment. Oracle Management Cloud was configured to deliver continuous performance monitoring, centralized log analytics, and machine learning based operational analytics, enabling administrators to shift away from repetitive maintenance activities. The platform provided dashboards, alerting, and automated remediation workflows, removing the low level routine tasks that had been consuming a majority of administrator time. According to Arvind Rajan Chief Executive Officer at Astute Business Solutions this automation ultimately helped the company deliver greater service to its customers. Operational scope centered on IT operations and service delivery teams within the professional services organization, with configuration and alerting workflows embedded into incident management practices. Governance adjustments included reassigning routine monitoring responsibilities and updating operator playbooks to take advantage of ML driven insights, aligning operational processes with the autonomous monitoring capabilities of the platform. The implementation underscores the relationship between Astute Business Solutions, Oracle Management Cloud, ML and Data Science Platforms, and core IT operations and service delivery functions.
GroupM Nordic	Professional Services	36000	$7.2B	United States	IBM	IBM Watson Studio (ex IBM Data Science Experience)	ML and Data Science Platforms	2016	n/a	In 2016, GroupM Nordic implemented IBM Watson Studio (ex IBM Data Science Experience) within the ML and Data Science Platforms category to accelerate advanced analytics and machine learning across its Nordic operations. The work centered on the Copenhagen based data science team and leveraged IBM technology to provide both rapid data exploration and production model workflows. The deployment combined IBM Watson Analytics for rapid data discovery with IBM Watson Studio to build, train and deploy models, incorporating interactive development environments such as Jupyter Notebooks and RStudio, and Watson Machine Learning for model experimentation and serving. Functional capabilities implemented included built in data discovery, collaborative notebooks, model versioning, automated model training pipelines and production deployment orchestration to support iterative prototyping. The architecture was implemented with flexible runtime options, able to run in the IBM Cloud, in a private cloud or on desktops, enabling elastic compute to ingest and process huge datasets. Integrations explicitly included ingestion of data from Google, Facebook and client sources, and the platform was used to operationalize models by deploying them directly on client websites to influence campaign behavior in near real time. Governance and process changes focused on creating a continuous reanalyze and monitoring loop for campaign performance, with teams constantly tweaking parameters and tracking results to refine hypotheses and models during campaigns. The program delivered faster time to production, with the first model moved into production in less than a month, and enabled a new client facing service that embeds machine learning into advertising execution and campaign intelligence.
	Professional Services	791000	$64.9B	Ireland	IBM	IBM Watson Studio (ex IBM Data Science Experience)	ML and Data Science Platforms	2017	n/a
	Professional Services	18000	$7.6B	Norway	IBM	IBM Watson Studio (ex IBM Data Science Experience)	ML and Data Science Platforms	2017	n/a
	Professional Services	100	$10M	United States	IBM	IBM Watson Studio (ex IBM Data Science Experience)	ML and Data Science Platforms	2016	n/a
	Professional Services	6	$1M	Netherlands	IBM	IBM Analytics for Apache Spark (Managed Cloud Spark Service)	ML and Data Science Platforms	2016	n/a
	Professional Services	150	$20M	United Kingdom	IBM	IBM Watson Studio (ex IBM Data Science Experience)	ML and Data Science Platforms	2015	n/a
	Professional Services	240	$119M	France	Dataiku	Dataiku Data Science Studio (DSS)	ML and Data Science Platforms	2017	n/a
	Professional Services	80	$10M	France	Dataiku	Dataiku Data Science Studio (DSS)	ML and Data Science Platforms	2017	n/a
	Professional Services	700	$135M	France	Dataiku	Dataiku Data Science Studio (DSS)	ML and Data Science Platforms	2017	n/a

Logo

Customer

Industry

Empl.

Revenue

Country

Vendor

Application

Category

When

VAR/SI

Insight

Insight Source

Astute Business Solutions

Professional Services

150

$20M

United States

Oracle

Oracle Management Cloud

ML and Data Science Platforms

2017

n/a

In 2017, Astute Business Solutions implemented Oracle Management Cloud, selecting Oracle Management Cloud within the ML and Data Science Platforms category to introduce autonomous monitoring and management for IT operations. The deployment used Oracle Management Cloud as a cloud hosted platform to apply machine learning for anomaly detection and automation of routine operational tasks across the IT environment. Oracle Management Cloud was configured to deliver continuous performance monitoring, centralized log analytics, and machine learning based operational analytics, enabling administrators to shift away from repetitive maintenance activities. The platform provided dashboards, alerting, and automated remediation workflows, removing the low level routine tasks that had been consuming a majority of administrator time. According to Arvind Rajan Chief Executive Officer at Astute Business Solutions this automation ultimately helped the company deliver greater service to its customers. Operational scope centered on IT operations and service delivery teams within the professional services organization, with configuration and alerting workflows embedded into incident management practices. Governance adjustments included reassigning routine monitoring responsibilities and updating operator playbooks to take advantage of ML driven insights, aligning operational processes with the autonomous monitoring capabilities of the platform. The implementation underscores the relationship between Astute Business Solutions, Oracle Management Cloud, ML and Data Science Platforms, and core IT operations and service delivery functions.

GroupM Nordic

Professional Services

36000

$7.2B

United States

IBM

IBM Watson Studio (ex IBM Data Science Experience)

ML and Data Science Platforms

2016

n/a

In 2016, GroupM Nordic implemented IBM Watson Studio (ex IBM Data Science Experience) within the ML and Data Science Platforms category to accelerate advanced analytics and machine learning across its Nordic operations. The work centered on the Copenhagen based data science team and leveraged IBM technology to provide both rapid data exploration and production model workflows. The deployment combined IBM Watson Analytics for rapid data discovery with IBM Watson Studio to build, train and deploy models, incorporating interactive development environments such as Jupyter Notebooks and RStudio, and Watson Machine Learning for model experimentation and serving. Functional capabilities implemented included built in data discovery, collaborative notebooks, model versioning, automated model training pipelines and production deployment orchestration to support iterative prototyping. The architecture was implemented with flexible runtime options, able to run in the IBM Cloud, in a private cloud or on desktops, enabling elastic compute to ingest and process huge datasets. Integrations explicitly included ingestion of data from Google, Facebook and client sources, and the platform was used to operationalize models by deploying them directly on client websites to influence campaign behavior in near real time. Governance and process changes focused on creating a continuous reanalyze and monitoring loop for campaign performance, with teams constantly tweaking parameters and tracking results to refine hypotheses and models during campaigns. The program delivered faster time to production, with the first model moved into production in less than a month, and enabled a new client facing service that embeds machine learning into advertising execution and campaign intelligence.