Software Engineering Case Studies

 

software engineering case study example

Jul 08,  · Software engineering for machine learning: a case study Amershi et al., ICSE'19 Previously on The Morning Paper we’ve looked at the spread of machine learning through Facebook and Google and some of the lessons learned together with processes and tools to address the challenges arising. Today it’s the turn of Microsoft. More specifically, we’ll be. The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypothesis testing and theory building. makbitas.gq case study contains a bias toward verification, that is, a tendency to . Do you know what a business case is--if not, here you'll find some free business case examples along with free templates to guide you when writing one. A business case can be used to show how a new product or process will be successful. Project managers can utilize business cases in the project planning phase to reveal how well the process or product conceived will be received and if indeed it Author: Jean Scheid.


Typical Business Case Examples


Software engineering for machine learning: a case study Amershi et al. A list of application areas includes search, advertising, machine translation, predicting customer purchases, voice recognition, image recognition, identifying customer leads, providing design advice for presentations and word processing documents, creating unique drawing features, healthcare, improving gameplay, sales forecasting, decision optimisation, incident reporting, bug analysis, fraud detection, and security monitoring.

As you might imagine, software engineering case study example, these are underpinned by a wide variety of different ML models. The teams doing the work are also varied in their make-up, some containing data scientists with many years of experience, and others just starting out.

To help spread these skills through the company a variety of tactics are used: a twice-yearly internal conference on machine learning and data science dedicates at least one day to the basics of technologies, algorithms, and best practices; internal talks are given year round on engineering details behind projects, and cutting-edge advances from academic conferences; several teams host weekly open forums on ML and deep learning; and there are mailing lists and online forums with thousands of participants.

A survey informed by conversations with 14 experienced ML leaders within Microsoft was sent to 4, members of those internal mailing lists, garnering replies. For simplicity the view in Figure 1 is linear, however, machine learning workflows are highly non-linear and contain several feedback loops. For example, if engineers notice that there is a large distribution shift between the training data and the data in the real world, they might want to go back and collect more representative data and rerun the workflow… This workflow can become even more complex if the system is integrative, containing multiple ML components which interact together in complex and unexpected ways.

The support a team requires changes according to their level of experience with ML, but regardless of experience levels, data availability, collection, cleaning, and management remains the number one concern! We identified three aspects of the AI domain that make it fundamentally different than prior application domains.

Their impact will require significant research efforts to address in the future. While the first two points are hopefully pretty self-explanatory, software engineering case study example, the third warrants a little more unpacking.

Maintaining strict module boundaries between machine learned models is difficult for two reasons. First, models are not easily extensible. For example, one cannot yet take an NLP model of English and add a separate NLP model for ordering pizza and expect them to work properly together… Second, models interact in non-obvious ways. This phenomenon is sometimes known as component entanglement and can lead to non-monotonic error propagation: improvements in one part of the system may actually decreases the overall system quality.

Thanks, Adrian. Sprints have no place in ML. Essentially, creative work demands experimentation and room to think which is not afforded by sprints. You are commenting using your WordPress. You are commenting using your Google account. You are commenting using your Twitter account. You are commenting using your Facebook account. Notify me of new comments via software engineering case study example. Notify me of new posts via email.

This site uses Akismet to reduce spam, software engineering case study example. Learn how your comment data is processed. Skip to content Software engineering for machine learning: a case study Amershi et al. Learnings and emerging best practices Having a seamless development experience covering possibly all the different stages in the process outlined above is important to automation.

But getting there is far from easy! The success of ML-centric projects depends heavily on data availability, quality, and management. Data sources are continuously changing and rigorous data versioning and sharing techniques are required.

Models have a provenance tag explaining which data it has been trained on software engineering case study example which version of the model was used. Datasets are tagged with information about where they came from and the version of the code used to extract it.

ML-centric software also sees frequent revisions initiated by model changes, parameter tuning, and data updates, the combination of which can have a significant impact on system performance. To address this, rigorous rollout processes are required. Model building should be integrated with the rest of the software development process, including common code repositories and tightly coupled sprints and stand-ups. Enlarge The big three We identified three aspects of the AI domain that make it fundamentally different than prior application domains.

Discovering, managing, software engineering case study example, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering. Like this: Like Software engineering case study example I think it would be of interest to our readers Thank you, software engineering case study example.

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software engineering case study example

 

The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypothesis testing and theory building. makbitas.gq case study contains a bias toward verification, that is, a tendency to . Do you know what a business case is--if not, here you'll find some free business case examples along with free templates to guide you when writing one. A business case can be used to show how a new product or process will be successful. Project managers can utilize business cases in the project planning phase to reveal how well the process or product conceived will be received and if indeed it Author: Jean Scheid. Software Engineering for Machine Learning: A Case Study Saleema Amershi Microsoft Research Redmond, WA USA [email protected] Andrew Begel For example, if engineers notice that there is a large distribution shift between the training data and the Cited by: 4.