Holger Mueller provides a one slide update on the acquisition of IBM by Red Hat, announced October 28th 2018. Key Takeaways, 3 pros and cons and a MyPOV.
IBM company history and other vital stats
International Business Machines Corporation is an American multinational technology company headquartered in Armonk, New York, United States, with operations in over 170 countries.
The company originated in 1911 as the Computing-Tabulating-Recording Company (CTR) and was renamed "International Business Machines" in 1924.
From Data to AI - Silicon Valley Open Source projects come to you - Madrid me...Luciano Resende
The IBM Center for Open Source, Data and AI Technology "CODAIT" (https://developer.ibm.com/code/open/centers/codait/) works on multiple open-source Data and AI projects. In this section we will introduce these projects around Jupyter Notebooks, reusable Model and Data assets, Trusted AI among others.
Presentation from 6th June 2017, covering IBM Systems PoV, WW Executive ownership of IBM Power Systems, challenges in our industry, Cloud, Hybrid Cloud, Intel's Strategic direction, IBM Power Systems strategic direction, OpenPOWER Foundation, OpenCAPI, Nutanix and POWER9
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apidays LIVE LONDON - The Road to Embedded Finance, Banking and Insurance with APIs
Old meets New - Managing transactions on the edge of the new world
Paul Lucas, IBM Distinguished Engineer and CTO for the Financial Services Sector at IBM
January 1, 2015. It is New Year’s Day and Virginia Rometty, IB.docxvrickens
January 1, 2015. It is New Year’s Day and Virginia Rometty, IBM’s chief executive officer (CEO),
looks at IBM’s performance and knows the company faces challenges. In October 2014, IBM announced
a tenth consecutive quarter of flat or declining sales, and she broke with prior CEO commitments to
earn more than $20 a share every year.1 IBM has a history of reacting to change slowly, but eventu-
ally reinventing itself. Under her predecessor, Sam Palmisano (2002–2011), IBM underwent significant
changes. Palmisano redefined IBM by deemphasizing hardware to increase the impact of consulting
and software. In 2002, IBM acquired PricewaterhouseCoopers Consulting for $3.5 billion and Rational
Software for $2.1 billion to remake IBM around three complementary areas of Systems and Financing,
Software, and Services (see Exhibit 1). Rometty was part of those changes and, since becoming CEO,
she has divested unprofitable businesses, such as selling its server unit to Lenovo for $2.1 billion in
2014, and paying Global Foundries $1.5 billion to take IBM’s money-losing chip business in 2014.2 Still,
a clear move by IBM into new profitable areas has remained elusive. Further, increased competition in
cloud computing services from Amazon and Google put pressure on software and services sales and
revenues (see Exhibit 2).
As she celebrates her third-year anniversary as CEO, Rometty becomes increasingly aware that time
is the one thing she is running out of to change IBM. Once again in its over-100-year history, IBM is at
the crossroads. It is time for further changes at IBM so it can adapt to disruptive cloud computing and
data analytics technologies. Alternatively, will the current strategy of offering customers integrated
technology solutions enable growing revenues from cloud computing and data analytics? As Virginia
Rometty is getting ready for the new year, she ponders: What needs to be done to avoid her becoming
known as the CEO that oversaw IBM’s decline or being dismissed early in her tenure?
Brief History of IBM
IBM’s (“Big Blue’s”) history dates back to long before electronic computers, reflecting several tech-
nological discontinuities (see Exhibit 3). It originated as the Tabulating Machine Company in 1896,
and it specialized in the development of punch-card data-processing equipment. Thomas J. Watson,
Sr. became general manager of the company in 1914 and in 1924 changed its name to International
Business Machines Corporation, or IBM. During the next 20 years, IBM grew rapidly. Despite the Great
Depression of the 1930s, IBM continued to manufacture new products, and after passage of the Social
FRANK T. ROTHAERMEL
KONSTANTINOS GRIGORIOU
GERMAN RETANA
DAVID R. KING
IBM at the Crossroads
Professors Frank T. Rothaermel, Konstantinos Grigoriou, German Retana, and David R. King prepared this case from public sources. This case is
developed for the purpose of class discussion. It is not intended to be used for any kind of ...
Continuous integration and deployment has become an increasingly standard and common practice in software development. However, doing this for machine learning models and applications introduces many challenges. Not only do we need to account for standard code quality and integration testing, but how do we best account for changes in model performance metrics coming from changes to code, deployment framework or mechanism, pre- and post-processing steps, changes in data, not to mention the core deep learning model itself?
In addition, deep learning presents particular challenges:
* model sizes are often extremely large and take significant time and resources to train
* models are often more difficult to understand and interpret making it more difficult to debug issues
* inputs to deep learning are often very different from the tabular data involved in most ‘traditional machine learning’ models
* model formats, frameworks and the state-of-the art models and architectures themselves are changing extremely rapidly
* usually many disparate tools are combined to create the full end-to-end pipeline for training and deployment, making it trickier to plug together these components and track down issues.
We also need to take into account the impact of changes on wider aspects such as model bias, fairness, robustness and explainability. And we need to track all of this over time and in a standard, repeatable manner. This talk explores best practices for handling these myriad challenges to create a standardized, automated, repeatable pipeline for continuous deployment of deep learning models and pipelines. I will illustrate this through the work we are undertaking within the free and open-source IBM Model Asset eXchange.
Fusion IO - Trends and Outlook (Sept 2013)JonCarvinzer
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IBM i at the heart of Cognitive SolutionsDavid Spurway
Presentation given on the 7th of March 2017, including IBM Systems PoV, IBM i Strategy Whitepaper, top IBM i client projects, IBM i 7.3, Roadmap, RPG and Open Source, customer examples combining IBM i and Linux and POWER9.
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Event Report - SAP Sapphire 2024 Orlando - lots of innovation and old challengesHolger Mueller
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Event Report - IBM Think 2024 - It is all about AI and hybridHolger Mueller
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IBM company history and other vital stats
International Business Machines Corporation is an American multinational technology company headquartered in Armonk, New York, United States, with operations in over 170 countries.
The company originated in 1911 as the Computing-Tabulating-Recording Company (CTR) and was renamed "International Business Machines" in 1924.
From Data to AI - Silicon Valley Open Source projects come to you - Madrid me...Luciano Resende
The IBM Center for Open Source, Data and AI Technology "CODAIT" (https://developer.ibm.com/code/open/centers/codait/) works on multiple open-source Data and AI projects. In this section we will introduce these projects around Jupyter Notebooks, reusable Model and Data assets, Trusted AI among others.
Presentation from 6th June 2017, covering IBM Systems PoV, WW Executive ownership of IBM Power Systems, challenges in our industry, Cloud, Hybrid Cloud, Intel's Strategic direction, IBM Power Systems strategic direction, OpenPOWER Foundation, OpenCAPI, Nutanix and POWER9
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January 1, 2015. It is New Year’s Day and Virginia Rometty, IB.docxvrickens
January 1, 2015. It is New Year’s Day and Virginia Rometty, IBM’s chief executive officer (CEO),
looks at IBM’s performance and knows the company faces challenges. In October 2014, IBM announced
a tenth consecutive quarter of flat or declining sales, and she broke with prior CEO commitments to
earn more than $20 a share every year.1 IBM has a history of reacting to change slowly, but eventu-
ally reinventing itself. Under her predecessor, Sam Palmisano (2002–2011), IBM underwent significant
changes. Palmisano redefined IBM by deemphasizing hardware to increase the impact of consulting
and software. In 2002, IBM acquired PricewaterhouseCoopers Consulting for $3.5 billion and Rational
Software for $2.1 billion to remake IBM around three complementary areas of Systems and Financing,
Software, and Services (see Exhibit 1). Rometty was part of those changes and, since becoming CEO,
she has divested unprofitable businesses, such as selling its server unit to Lenovo for $2.1 billion in
2014, and paying Global Foundries $1.5 billion to take IBM’s money-losing chip business in 2014.2 Still,
a clear move by IBM into new profitable areas has remained elusive. Further, increased competition in
cloud computing services from Amazon and Google put pressure on software and services sales and
revenues (see Exhibit 2).
As she celebrates her third-year anniversary as CEO, Rometty becomes increasingly aware that time
is the one thing she is running out of to change IBM. Once again in its over-100-year history, IBM is at
the crossroads. It is time for further changes at IBM so it can adapt to disruptive cloud computing and
data analytics technologies. Alternatively, will the current strategy of offering customers integrated
technology solutions enable growing revenues from cloud computing and data analytics? As Virginia
Rometty is getting ready for the new year, she ponders: What needs to be done to avoid her becoming
known as the CEO that oversaw IBM’s decline or being dismissed early in her tenure?
Brief History of IBM
IBM’s (“Big Blue’s”) history dates back to long before electronic computers, reflecting several tech-
nological discontinuities (see Exhibit 3). It originated as the Tabulating Machine Company in 1896,
and it specialized in the development of punch-card data-processing equipment. Thomas J. Watson,
Sr. became general manager of the company in 1914 and in 1924 changed its name to International
Business Machines Corporation, or IBM. During the next 20 years, IBM grew rapidly. Despite the Great
Depression of the 1930s, IBM continued to manufacture new products, and after passage of the Social
FRANK T. ROTHAERMEL
KONSTANTINOS GRIGORIOU
GERMAN RETANA
DAVID R. KING
IBM at the Crossroads
Professors Frank T. Rothaermel, Konstantinos Grigoriou, German Retana, and David R. King prepared this case from public sources. This case is
developed for the purpose of class discussion. It is not intended to be used for any kind of ...
Continuous integration and deployment has become an increasingly standard and common practice in software development. However, doing this for machine learning models and applications introduces many challenges. Not only do we need to account for standard code quality and integration testing, but how do we best account for changes in model performance metrics coming from changes to code, deployment framework or mechanism, pre- and post-processing steps, changes in data, not to mention the core deep learning model itself?
In addition, deep learning presents particular challenges:
* model sizes are often extremely large and take significant time and resources to train
* models are often more difficult to understand and interpret making it more difficult to debug issues
* inputs to deep learning are often very different from the tabular data involved in most ‘traditional machine learning’ models
* model formats, frameworks and the state-of-the art models and architectures themselves are changing extremely rapidly
* usually many disparate tools are combined to create the full end-to-end pipeline for training and deployment, making it trickier to plug together these components and track down issues.
We also need to take into account the impact of changes on wider aspects such as model bias, fairness, robustness and explainability. And we need to track all of this over time and in a standard, repeatable manner. This talk explores best practices for handling these myriad challenges to create a standardized, automated, repeatable pipeline for continuous deployment of deep learning models and pipelines. I will illustrate this through the work we are undertaking within the free and open-source IBM Model Asset eXchange.
Fusion IO - Trends and Outlook (Sept 2013)JonCarvinzer
How does Fusion IO get out of the mess they are in. What direction will they take? How does the competition look now? Can they survive on their own? What is the gross margin and profitability expectation? How do they fix their issues? Will they be bought out?
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