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The 7 Stages of Machine Learning are: Data Preparation. Data Visualization. ML Modeling. Feature Engineering.
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What are the 3 stages of machine learning?

Stage 1 : Collect, prepare and sense data. stage 2 : Use data to answer questions. stage 3 : Create predictive applications.
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How many types of learning are there in ML?

3 types of machine learning – Machine learning involves showing a large volume of data to a machine so that it can learn and make predictions, find patterns, or classify data. The three machine learning types are supervised, unsupervised, and reinforcement learning.
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What is the last stage of ML?

3. Model Development – Once you have the data prepared, it’s time to develop the model. Model preparation is at the core of the machine learning life cycle, and it involves three subpoints:

Model Selection and Assessment. The first step is selecting the type of model to be used for development. Data scientists usually fit and test different models to see which one performs better. Typically, they choose the model (classification model, regression model, etc.) based on the type of data they have and the one that has the highest accuracy rate.

Model Training. In this phase, data scientists start to do experiments with the model. They input the data into an algorithm to extract outputs. In this step, the first signs of the final output are visible, which also helps to modify the model accordingly to assign better predictions.

Model Evaluation. After the model is done training, the final stage includes evaluating metrics like accuracy and precision to measure the model’s performance. It also includes an in-depth analysis of the errors and biases. This allows analysts to come up with solutions to eliminate them. If needed, data scientists re-run the model after making the necessary improvements to improve accuracy and performance.

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What are the 4 stages of AI process?

Did you know there are four distinct types of artificial intelligence? These four types aren’t all created equal: Some are far more sophisticated than others. Some of these types of AI aren’t even scientifically possible right now. According to the current system of classification, there are four primary AI types: reactive, limited memory, theory of mind, and self-aware.
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What is step 5 in machine learning?

5. Train Model – Now the next step is to train the model, in this step we train our model to improve its performance for better outcome of the problem. We use datasets to train the model using various machine learning algorithms. Training a model is required so that it can understand the various patterns, rules, and, features.
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What are the 6 stages of learning?

Background Information – In 1956, Benjamin Bloom with collaborators Max Englehart, Edward Furst, Walter Hill, and David Krathwohl published a framework for categorizing educational goals: Taxonomy of Educational Objectives, Familiarly known as Bloom’s Taxonomy, this framework has been applied by generations of K-12 teachers and college instructors in their teaching.

• The framework elaborated by Bloom and his collaborators consisted of six major categories: Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation.
• The categories after Knowledge were presented as “skills and abilities,” with the understanding that knowledge was the necessary precondition for putting these skills and abilities into practice.

While each category contained subcategories, all lying along a continuum from simple to complex and concrete to abstract, the taxonomy is popularly remembered according to the six main categories.
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What are the 4 learning types?

There are 4 predominant learning styles: Visual, Auditory, Read/Write, and Kinaesthetic. While most of us may have some general idea about how we learn best, often it comes as a surprise when we discover what our predominant learning style is.
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What is an ML in education?

Machine Learning in Education Machine learning (ML) is transforming education and fundamentally changing teaching, learning, and research. Educators are using ML to spot struggling students earlier and take action to improve success and retention. Researchers are accelerating research with ML to unlock new discoveries and insights.

1. ML is expanding the reach and impact of online learning content through localization, transcription, text-to-speech, and personalization.
2. Lastly, AWS is working with leaders in the public sector to adapt to the new world of ML and better equip students with the skills and expertise they need to succeed.

Identify and attract the right students, forecast enrollment, predict outcomes, and ensure student success. Accelerate research by making ML simpler and less costly to use across research activities and disciplines. Modernize the campus experience and make them smarter, safer, and more efficient. Identify at-risk students and target interventions. Enhance campus security with ML-powered threat detection and response. Improve teacher efficiency and impact with personalized content and AI-enabled teaching assistants and tutors. Improve efficiency of assessments and grading. Provide online self-service tools for parents and students. Reach a global audience and serve users of all abilities via transcription, translation, text-to-speech, and content classification. Develop personalized learning experiences, self-service capabilities, and chatbots. Target marketing and advertising and track and predict student outcomes. Machine learning is transforming how education institutions are tracking student performance and spotting issues. ML-powered personalized learning approaches are enabling educators to tailor learning pathways to individual students. Institutions are using ML to enhance the campus experience and enable self-service capabilities.

1. Traditionally, video lectures, discussion groups, and other high-velocity online learning content has been cost-prohibitive for transcription and translation at scale.
2. Deep learning powered translation, transcription, and text-to-speech services provide accurate and low-cost options to make content accessible to students around the globe.

Machine learning is moving out of the computer science department across all research disciplines. Research teams without deep data science and ML expertise can leverage ML to accelerate research and drive discovery. ML is improving content search and discovery for scientific and research document repositories.

Attract the right students for admissions and accurately forecast enrollment to optimize capacity. Prevent fraud and protect student and staff safety both online and offline. Manage facilities and equipment more effectively through ML-powered predictive maintenance. Check out our series of ML webinars.

Improve Education Outcomes on-demand covers how to enable adaptive learning, recommendation and personalization engines, content search/discovery/publishing, content translation/localization, content moderation, and natural language processing. Course Hero is an online learning platform that provides students access to over 25 million course-specific study materials, including study guides, class notes, and practice problems for numerous subjects.

The platform, which runs on AWS, is designed to enable every student to take on their courses feeling confident and prepared. Amazon SageMaker checks and validates the documents for fraud, honor code violations, copyright infringements, and spam – while saving its human employees for higher-order work.

Echo360 is a global leader in active and distance learning solutions. With Amazon Transcribe’s Automatic Speech Recognition, Echo360 is improving the learning experience for all students by developing capabilities for smarter search, deeper ways to engage with content, and a pathway for colleges and universities to offer effective and affordable closed captioning for all academic video.
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How many stages of learning are there?

The Four Phases of Learning In the training world, the optimum number seems to be “four.” There are four learning styles, four stages of competence, and a four-level evaluation model.

There is also a four-phase learning cycle.According to Dave Meier, in The Accelerated Learning Handbook, here is what the cycle encompasses: Preparation: Arousing Interest Presentation: Encountering the New Knowledge or Skills Practice: Integrating the New Knowledge or Skills Performance: Applying the New Knowledge and Skills

Meier says that unless all four phases of learning are present in in one form or another, no real learning occurs. Here’s a breakdown.
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How many days till ML season ends?

When Will Mobile Legends Ranked Season 21 End?

The Mobile Legends: Bang Bang Ranked Season 21 is expected to end on 23rd Sept 2021, according to the in-game counter. Moonton is yet to officially announce the end date of Mobile Legends Ranked Season 21. The Mobile Legends Ranked Season 21 exclusive skin is yet to be revealed.

The Mobile Legends: Bang Bang Ranked Season 21 is expected to end on 23rd Sept 2021, according to the in-game counter. However, players have to keep in mind that Moonton is yet to officially announce the end date of Mobile Legends Ranked Season 21, so take this information with a grain of salt.

As usual, players who reach Master rank and above will receive an exclusive skin for their efforts. The Mobile Legends Ranked Season 21 exclusive skin is yet to be revealed. In the meantime, players still have a lot of time to climb the ranked ladder and secure their free skin after the season ends. The ranked exclusive skin for Mobile Legends Ranked Season 21 is yet to be revealed by Moonton.

The competitive rank season of Mobile Legends usually lasts around three months. Every season boasts an exclusive skin that can only be obtained in that season and will no longer be made available once a new season starts. The current Mobile Legends Ranked Season 21 officially kicked off on 19th June 2021. The previous season rewarded players with the Kaja “Crow Magician” exclusive skin for players who reach Master rank and above during Season 20. The skin features a red robe, a sinister-looking hat with a face, and a mask that resembles a 17-century beaked mask that doctors wore during the European plague.

1. The skin emits a mysterious aura that can intimidate opponents.
2. The hero is not included with the skin, so players will need to purchase the hero Kaja before using the skin.
3. Given how visually appealing the previous ranked season skins are, fans can expect the upcoming exclusive skin to be as flashy as the previously released ranked season skins.

Fans will have to keep their eyes peeled for more updates in the coming weeks. It will be interesting to see which hero will receive the upcoming Mobile Legends Ranked Season 21 exclusive skin. In the meantime, players still have time to grind their way to Master rank and above.
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How long does it take for ML to end season?

How long is the season in Mobile Legends? – In the Mobile Legends game, one season will last for 3 months. This means that each season will be around 90 days. So if in a year, you will find 4 seasons in Mobile Legends. That means there will be 4 seasonal skins that you can get in a full year. Which skin is one of the targets for players in Mobile Legends.

Regarding this, of course, it is very interesting to do. Then how do you get the season’s skin? The method itself is fairly easy, you only need to play Mobile Legends in ranked mode, even in one play you can still get it as long as your rank is above the Master Rank. Especially in 2021, you will meet the next four seasons.

For now it is Season 19. Which season 19 in Mobile Legends will end on March 20. Season 19 is divided into January, February to March 2021. Surely you guys know about this season’s problem. So that later you can guess when the Season in Mobile Legends will end.

• It’s not clear how long the season in Mobile Legends is.
• With this, of course, you already know everything about the season, especially in MOBA games like Mobile Legends.
• So, that seems to be a review of how long the season in Mobile Legends is.
• Make sure that every season you always play Mobile Legends so you can get seasonal skin prizes every season.

Don’t forget to keep practicing, play wisely and don’t become a toxic player! Don’t forget to follow our social media on Instagram
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How many stages are in AI?

AI is divided broadly into three stages : artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial super intelligence (ASI).
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What are the 2 types of machine learning techniques?

How Machine Learning Works – Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. Figure 1. Machine learning techniques include both unsupervised and supervised learning.
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What are the 2 types of machine learning models?

Classification of Machine Learning Models: Supervised Learning. Unsupervised Learning.
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What are the three essential components of a learning system in ML?

Learning is the Result of Representation, Evaluation, and Optimization – The field of machine learning has exploded in recent years and researchers have developed an enormous number of algorithms to choose from. Despite this great variety of models to choose from, they can all be distilled into three components.

The three components that make a machine learning model are representation, evaluation, and optimization. These three are most directly related to supervised learning, but it can be related to unsupervised learning as well. Representation – this describes how you want to look at your data. Sometimes you may want to think of your data in terms of individuals (like in k-nearest neighbors) or like in a graph (like in Bayesian networks).

Evaluation – for supervised learning purposes, you’ll need to evaluate or put a score on how well your learner is doing so it can improve. This evaluation is done using an evaluation function (also known as an objective function or scoring function ).
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What are the three steps in the 3S model?

A gap has emerged in teaching artificial intelligence (AI) in business education, where a style of curriculum based on strategy is missing. This article presents a new framework, the 3S Process, as a method for teaching leaders how to strategically adopt AI within their organizations.

• At a high-level, the 3S Process consists of three stages (Story, Strategy, and Solution), which are described in detail in the article.
• Stage 1: Story in the process is inspired by the Harvard Case Method to provide context for a problem.
• Stage 2: Strategy uses Design Thinking to produce candidate solutions.

The substage of Empathy in Design Thinking plays a crucial role to reduce bias in designing AI. Virtualization technology is a tool for students to experience hands-on learning in prototype development. Stage 3: Solution is where students advocate for their conceptual AI solution in the context of the case study.

1. AI is a type of complex system; therefore, students should consider feedback loops and the potential for unintended biases to enter a deployed solution.
2. The presentation of the 3S Process in this article is conceptual.
3. Further empirical studies, including evaluations of the 3S Process in classroom settings, will be considered in the future.

Introduction There is a growing interest in teaching artificial intelligence (AI) and machine learning (ML) in business schools around the world (S.-W., 2018). However, an acclaimed approach to teaching AI (Figure 1) in the context of business, especially in terms of entrepreneurship, remains elusive. Figure 1. AI Venn Diagram. Based on the author’s experience working with numerous corporations of varying size, current Master of Business Administration (MBA) programs that include AI can be grouped according to three styles of curricula: 1. General Technology (providing a broad overview of AI techniques), 2.

1. Specialized Technology (in-depth instructing of AI algorithms, data science, and optimization), and, 3.
2. Decision Making (using AI/ML to inform the decision-making process).
3. A fourth style based on strategy is missing from approaches to business education.
4. How should leaders be educated in strategically adopting AI/ML in their organizations, and within their products and services (Stachowicz-Stanusch & Wolfgang, 2019)? Watkins writes, “A business strategy is a set of guiding principles that, when communicated and adopted in the organization, generates a desired pattern of decision making” (2007).

To glean the most from AI, it should be adopted strategically in organizations to solve business problems (and not just be another piece of technology), in order to garner exponential benefits overtime. The goal of this article is to provide a significant step towards addressing these problems by providing a new framework for a strategy-based approach, referred to as the 3S Process (Bhalla, 2019).

At a high-level, the 3S Process consists of story, strategy, and solution (Figure 2). The 3S Process is inspired by the Harvard Case Method (Rebeiz, 2011) and the approach of Design Thinking (Brown, 2009). The case method provides the context for an example problem, and Design Thinking provides a strategic process for developing a considered solution.

Design Thinking has been shown as an effective tool in business education, and in particular, in entrepreneurship education (Brown & Katz, 2011). Figure 2.3S Process Methodology One of the aims of this work is to understand how to develop AI/ML in order to innovate products and services, and ultimately grow organizations. The 3S Process is the result of codifying the author’s experience in teaching technical, graduate-level courses in AI and ML (in computer science departments at universities), and the author’s experience in consulting with business and technical leaders (C-suite executives) in small to medium enterprises (SMEs).

It was observed that although many organizations wanted to adopt AI, it was not clear to them how to adopt AI. This observation fits with a survey of thousands of executives about how their companies use AI, and the data shows that only 8% of firms engage in core practices to support widespread adoption of AI (Fountaine et al., 2019).

The author’s objective was to devise a step-by-step process, which was based on commonly known educational techniques and strategic practices, to enable delivery of an approachable framework. Framework: The 3S Process Stage 1: Story is based on the Harvard Case Method.

Broadly, there are four types of case situations (Ellet, 2007): • Problems, • Decisions, • Evaluations, and, • Rules. For the purposes of the 3S Process, only case types of problems are considered (since other case types are not applicable). The intention of using a case method is to set the context of the problem to be solved.

Harvard Business School (HBS) is in the midst of creating their own set of AI cases (Kenny, 2018). It will be interesting to see how HBS frames their AI cases (as well as other business schools that use case methods), and if/how the AI cases extend beyond typical problems.

• Stage 2: Strategy is inspired by the approach of Design Thinking.
• Design Thinking was originally conceptualized for the design of physical products (Brown, 2008).
• Over time, Design Thinking has been applied not just to the field of industrial design, but to several others also, including the design of businesses themselves (Martin, 2009).

Since its inception, there have been many variations and extensions to Design Thinking, each suited to a specific type of problem (Tschimmel, 2012). In this work, the original description of Design Thinking is used, which has five phases: • Empathy, • Define, • Ideate, • Prototype, and, • Test.

Stage 3: Solution is the result of the Design Thinking approach within the context of a specific story. It is important to note that arriving at a solution is in actuality building an AI system (Meadows, 2008), which is integrated into another product or service. The performance, or even the behaviour itself, of the system may change with use, for example, the collection and variation of data over time.

To navigate through the framework, the 3S Process is subdivided into nine substages (Figure 3). The graph, with substages as nodes and with transitions from one substage to another as directed edges, represents common paths through the 3S Process. The connectivity (traversals through the graph) should be adapted to the problem to be solved. Figure 3. Graph of the nine substages of the 3S Process. Stage 1: Story – Scenario A case study, provided by educators to students, establishes the context of the problem space. Equally as important, the case study is the basis for discussion between students and educators.

• Stage 1: Story – Research Conduct research to better understand the problem space.
• What are the important details regarding the problem? What aspects of the problem space can be ignored? Narrow the scope of the problem, focus.
• Stage 2: Strategy – Empathy Understand the potential biases, for example, training data, particular algorithms, and potential users.

Examine the problem from multiple opposing viewpoints (Martin, 2009). What are the privacy and security concerns? Stage 2: Strategy – Define What exactly is the problem to be solved? Define a set of quantitative/qualitative metrics to measure the success of a solution for solving the problem.

Stage 2: Strategy – Ideate Brainstorm several candidate solutions. What are the available resources (for example, data and infrastructure)? If a full, candidate solution cannot be implemented as a prototype in a classroom setting, can a subset of the problem be addressed? Stage 2: Strategy – Prototype Ideally, a prototype should be designed quickly and implemented efficiently.

Fast prototyping leads to the possibility for a greater number of iterations of the Ideate-Prototype-Test cycle. Stage 2: Strategy – Test Perform quantitative and qualitative measurements to evaluate the level of success of the candidate solution. If possible, compare the candidate solution to other solutions that were tested previously, and compare to other solutions in the market (or discussed in the case study).

1. Stage 3: Solution – Deploy In the context of the case study, make persuasive arguments for the reasoning behind the selected solution.
2. How would the adoption of the selected solution be marketed externally of the organization, or sold internally within the organization? How would the performance of the selected AI system be monitored over time? Stage 3: Solution – Feedback How will the transition from training data to continuous data be managed? What derived data can be realized? Important Features of the Framework There are three important features of this framework.

First, the step of Empathy in Design Thinking is used to help address ethical issues when developing and deploying an AI solution. Second, a software stack using virtualization technology is discussed for how AI prototypes can be developed in practice.

Third, complex systems are examined, since even a simple set of rules and algorithms can lead to unpredictable results. Complexity is an important, but often ignored aspect of AI, which is ultimately the pursuit of designing a complex system that displays agency. Empathy One of the greatest aspects to Design Thinking is in the phase Empathy.

The ability for a designer to empathize with the end customer (and other stakeholders in the design-production-consumption process) for a product in the context of its environment leads to more human-centric and sustainable solutions. In the 3S Process, the designer is to be empathetic to reduce bias in the end solutions, be it for human-to-machine or machine-to-machine interfaces.

1. For example, Microsoft Inc.
2. Released Tay, a chat-bot, in March 2016 (Johnston, 2017).
3. Tay used Twitter as the interface to converse with humans.
4. By people posting offensive Tweets to Tay, the chat-bot quickly learned and then started to post its own inflammatory Tweets.
5. Tay was taken down after only 16 hours of public operation.

By employing the stage of Empathy to this research project, the developers could have anticipated the possibility of such an outcome and could have added measures to their AI chat-bot to mitigate bias. Virtualization While Stage 1: Story, with the case method at its core, is purely an intellectual exercise, Stage 2: Strategy offers the opportunity for learning through practical examples and exercises with software.

• It would be difficult, if not impossible due to time constraints, for students (for example, in an MBA course on AI) to implement a full-fledged AI system in the context of solving a case study problem.
• Instead, the emphasis should be on implementing a solution that addresses a subproblem, as a way to gain experience in AI through hands-on learning.

Virtualization software, for example, Docker (Boettiger, 2015), can be used as part of the Ideate-Prototype-Test substages of the 3S Process. Docker performs operating-system-level virtualization and runs software packages referred to as containers. Containers are isolated from each other and bundle their own application, tools, libraries, and configuration files.

Containers can communicate with each other using specific channels and message passing. Docker works with operating systems that run on desktop personal computers and servers. Therefore, the focus here can be thought of as AI running in the cloud and not at the edge (that is, embedded AI). The idea here is that educators develop software that is built on top of virtualization technology (Figure 4), thus allowing students to focus on the code, algorithms, and concepts needed to build prototypes to address specific subproblems.

Depending on the technical know-how of the students, they could work at a high-level (that is, determining effects based on adjusting parameters), at a low-level (write the code for specific algorithms), or somewhere in between these two positions. Figure 4. Docker stack. There are five advantages to using virtualization software from an educational perspective.1. Cross-platform. This allows the software to be available to a wider audience, and independent of the host operating systems (macOS, Windows, and many distributions of the Linux operating system).2.

1. Software bundles.
2. The particular software needed can be used and pre-configured (for example, pre-populating a database).3.
3. Customizable.
4. Specific applications can be written that run on top of Docker (for example, Python programs, which can use the vast number of AI/ML packages that are readily available).4.

Modular. Each software bundle running on top of Docker can be developed and updated independently, meaning that educators can take a step-by-step approach to creating curriculum.5. Cloud-ready. Containers can be integrated into web services for production (that is, use the code that was developed for a prototype as part of the code base for the solution).

Furthermore, developing web services offer the opportunity to integrate with other cloud services (for example, Amazon Web Services, Microsoft Azure, Google Cloud, IBM Watson), through application programming interfaces (APIs), resulting in faster prototyping, access to pre-trained AI models, and continuously receiving new capabilities.

Interoperability between web services based on virtual containers is one of the best methods to realize powerful, complex AI systems today. Complexity Stage 1: Story is based on the Harvard Case Method to provide context to a problem space, and Stage 2: Strategy uses Design Thinking and virtualization to develop practical prototypes to address subproblems.

Stage 3: Solution completes the 3S Process. Since it is not reasonable for students to implement a production-ready AI system in a classroom setting, the best practice would be for students to develop persuasive arguments for their particular, conceptual solutions, and try to anticipate unintended consequences.

Unexpected behaviour can occur in AI due to it being a type of complex system. Mitchell defined a “complex system” this way: “A system in which large networks of components with no central control and simple rules of operation give rise to complex collective behaviour, sophisticated information processing, and adaptation via learning or evolution.” (2009) Information returning to an AI system can be considered as either as a positive feedback loop (amplification) or a negative feedback loop (dampening).

It is critical to understand the information returning to the system, the correct method to process the information, and the best practice to store the information. For example, unexpected feedback changed the behaviour of the chat-bot Tay, as discussed previously. Conclusion To summarize, this article presents a new framework, the 3S Process, for teaching AI in the context of business education.

Stage 1: Story uses the Harvard case method to set the context of the problem space. Students are expected to engage in discussion to further understand the problem at hand, to uncover details and narrow the scope of the problem space. Stage 2: Strategy is based on the approach of Design Thinking to develop a prototype, which for practical purposes in a classroom setting addresses a subproblem unveiled in the case study.

Particular emphasis is placed on the substage of Empathy to reduce potential biases in the final AI system. Furthermore, virtualization software can be used to create practical candidate solutions, and thus provide a hands-on learning opportunity for the Ideate-Prototype-Test substage cycle. Stage 3: Solution is where students advocate for their conceptual AI solution in the context of the case study and describe their Design Thinking thought process to reach their AI solution.

Students should remember that AI is a type of complex system and postulate potential feedback loops, while taking into account the potential for unintended biases to enter the system. When educators use the 3S Process the expectation should not be that business students develop a deep, technical understanding of AI.

1. Instead, the hope is that the 3S Process provides students with critical thinking and hands-on experience with AI, so that they can make more informed strategic decisions about AI as leaders in their future organization and as part of teams.
2. Business education using the 3S Process can equip leaders with common language and understating regarding AI, thereby improving communication between management and technical experts.

It should be noted that the 3S Process can be adapted from use in education to be applied to entrepreneurship. Instead of using a case study, Stage 1: Story is based on the business problem to be solved and context is provided by market realities. Instead of addressing a subproblem, Stage 2: Strategy directly addresses the business problem.

As with the education case, leaders should be aware of bias in the business case as well. The use of virtualization software at this stage has a real benefit, as it can be transferred with ease to production, Stage 3: Solution, particularly for cloud services. Leaders will have to sell their solutions internally within their organization and measure external market response.

The Five stages of implementing ML project

Complexity will still play a factor and require leaders to continually monitor the performance of their AI system. Finally, the 3S Process is a complex network itself. The author’s intent is that leaders can leverage the 3S Process, and that the resulting collective behaviour will lead to the emergence of creative thinking around integrating AI in business.

1. Acknowledgements Many thanks to the two anonymous reviewers & TIM Review editors for their valuable feedback.
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Boston, MA, Harvard Business School Publishing. Meadows, D.H.2008. Thinking in Systems: A Primer. White River Junction, VT, Chelsea Green Publishing. Mitchell, M.2009. Complexity: A Guided Tour. Oxford, Oxford University Press. Rebeiz, K.S.2011. An Insider Perspective on Implementing the Harvard Case Study Method in Business Teaching.

US-China Education Review A, 5: 591-601. Stachowicz-Stanusch, A. & Wolfgang, A. (Eds.) 2019. Management and Business Education in the Time of Artificial Intelligence: A Need to Rethink, Retrain, and Redesign. Charlotte, Information Age Publishing. Tschimmel, K.2012. Design Thinking as an effective Toolkit for Innovation.

XXIII ISPIM Conference: Action for Innovation: Innovating from Experience, 2012 Barcelona. Watkins, M.D.2007. Demistifying strategy: The What, Who, How, and Why. Harvard Business Review. Figure 1. AI Venn Diagram. Figure 1. AI Venn Diagram. Figure 2.3S Process Figure 4. Docker stack.
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