AI News, Bridging the divide: Business users and machine learning experts

Bridging the divide: Business users and machine learning experts

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.

Most media stories emphasize a need for expertise in algorithms and quantitative techniques (machine learning, statistics, probability), and yet the reality is that expertise in advanced algorithms is just one aspect of industrial data science.

We talked about her background, techniques for evaluating machine learning models, how much math data scientists need to know, and the art of interacting with business users.

As excited as I am about the growing number of tools that open up analytics to business users, the interplay between data experts (data scientists, data engineers) and domain experts remains important.

Zheng recounts her experience working with business analysts: It’s not enough to tell someone, “This is done by boosted decision trees, and that’s the best classification algorithm, so just trust me, it works.”

I thought the best thing that I could do is to build software that doesn’t take a machine learning expert to use, so that the domain experts can use them to build their own applications.

It’s a common adage among experienced data scientists that “good features allow a simple model to beat a complex model.”

One of the major reasons why many find deep learning so compelling is that it provides a mechanism for learning a hierarchy of representations across layers.

You really have to process the features, do a lot of pre-processing, and first do things like extract out the frequent sequences, maybe, or figure out what’s the right way to represent IP addresses, for instance.

In her forthcoming O’Reilly report, Zheng provides a nice framework for evaluating machine learning models: If we think of training the model as a part of it, then even after you’ve trained a model and evaluated it and found it to be good by some evaluation metric standards, when you deploy it, where it actually goes and faces users, then there’s a different set of metrics that would impact the users.

Machine Learning

Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.

The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.

Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.

Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.

Getting Value from Machine Learning Isn’t About Fancier Algorithms — It’s About Making It Easier to Use

Machine learning can drive tangible business value for a wide range of industries— but only if it is actually put to use.

Despite the many machine learning discoveries being made by academics, new research papers showing what is possible, and an increasing amount of data available, companies are struggling to deploy machine learning to solve real business problems.

Our team decided to address this problem by finding patterns in a complex volume of data, building machine learning models, and using them to anticipate the occurrence of critical problems.

The model identified red flags that might indicate an upcoming problem in project performance, including an increase in the average time spent resolving a bug, and backlog processing and resolution time.

This lead time allows service provider teams to determine the nature of the upcoming problem, identify the areas that would be impacted, and take remedial actions to prevent it from occurring at all.

Currently, the AI project manager (tested and integrated in Accenture’s myWizard Automation Platform across delivery projects) serves predictions on a weekly basis and correctly predicts red flags 80% of the time, helping to improve KPIs related to project delivery.

The next step for the project will be to use the same data to create models that can predict cost overruns, delays in the delivery schedule, and other critical aspects of project execution that are critical to the business performance of an organization.

Instead, our biggest requirements were for a robust software engineering practice, automation that allowed domain experts to come in at the right level, and tools that could support comprehensive model testing.

Greater involvement of domain experts: Domain experts determined key variables — for instance, which specific events posed a risk to project performance, how far ahead the model had to be able to predict for the information to be valuable, and which past projects should be used to train the model.

Domain experts are often better than machines at suggesting patterns that hold predictive power — for example, an increase in the average response time for a ticket could eventually lead to poor project performance;

The automated testing suite built into ML 2.0 gave the deployment team the flexibility to simulate previous states of the data, add data that had been withheld from the development process, and conduct their own tests for several points in time.

Artificial Intelligence (AI) and Machine Learning are projected to become mainstream technologies in the coming years, and are clearly already having a significant impact across many industries.

The modern Data Scientist, armed with the power of an open source, algorithm economy, are becoming important parts of numerous organizations around the world.

The transformative power of AI and ML have already been perceived in customer service (digital assistants), in telemedicine (assisted patient care), in banking and finance (robot sales representatives), or in manufacturing (robot assembly-line workers).

Moving into the Business Office As AI technologies gradually started moving toward statistics-enriched solutions, the biggest stumbling block that surfaced was limited data.

In fact, Data Scientists will be required to intervene when advanced data solutions or unique data solutions must be designed to accomplish complex business goals.

According to this analysis, computer science, software programming, statistics, Data Modeling, using ML libraries, and system design have been identified as core skills for preparing to be an ML expert.

In Introduction to Machine Learning Algorithms, discusses introductory discourse on ML and how Machine Learning algorithms aspire to emulate the human brain functions by closely studying available data patterns.

The ultimate goal of these smart machines is to explore and discover how the human brain studies, organizes, and interprets information to arrive at conclusions or make future predictions.

 The article indicates that the choice of an ML algorithm to apply largely depends on the user’s domain knowledge, available data, and the desired results.

The blog post titled Machine Learning Strategy 7 Steps  suggests that perhaps Data Scientists and ML experts need to work together to complete the entire model building exercise from data acquisition/cleaning to finishing the model.

large sample of available literature suggests that modern Data Scientists and ML Engineers need to be as technically qualified as business savvy to succeed in designing real-world solutions for the business world.

With the mainstreaming of Big Data and rise of sensor-aided streaming data, the sheer volume of available data has helped ML algorithms to prosper and continuously improve on the existing models.

Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models Paperback – May 25, 2018

if(typeof tellMeMoreLinkData !== 'undefined'){

A.state('lowerPricePopoverData',{'trigger':'ns_WNDTWR7C26RWNBX0CTPK_27015_1_hmd_pricing_feedback_trigger_product-detail','destination':'/gp/pdp/pf/pricingFeedbackForm.html/ref=_pfdpb/147-2279433-2379714?ie=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&ASIN=1788621115&PREFIX=ns_WNDTWR7C26RWNBX0CTPK_27015_2_&WDG=book_display_on_website&dpRequestId=WNDTWR7C26RWNBX0CTPK&from=product-detail&storeID=booksencodeURI('&originalURI=' + window.location.pathname)','url':'/gp/pdp/pf/pricingFeedbackForm.html/ref=_pfdpb/147-2279433-2379714?ie=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&ASIN=1788621115&PREFIX=ns_WNDTWR7C26RWNBX0CTPK_27015_2_&WDG=book_display_on_website&dpRequestId=WNDTWR7C26RWNBX0CTPK&from=product-detail&storeID=books','nsPrefix':'ns_WNDTWR7C26RWNBX0CTPK_27015_2_','path':'encodeURI('&originalURI=' + window.location.pathname)','title':'Tell Us About a Lower Price'});

return {'trigger':'ns_WNDTWR7C26RWNBX0CTPK_27015_1_hmd_pricing_feedback_trigger_product-detail','destination':'/gp/pdp/pf/pricingFeedbackForm.html/ref=_pfdpb/147-2279433-2379714?ie=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&ASIN=1788621115&PREFIX=ns_WNDTWR7C26RWNBX0CTPK_27015_2_&WDG=book_display_on_website&dpRequestId=WNDTWR7C26RWNBX0CTPK&from=product-detail&storeID=booksencodeURI('&originalURI=' + window.location.pathname)','url':'/gp/pdp/pf/pricingFeedbackForm.html/ref=_pfdpb/147-2279433-2379714?ie=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&ASIN=1788621115&PREFIX=ns_WNDTWR7C26RWNBX0CTPK_27015_2_&WDG=book_display_on_website&dpRequestId=WNDTWR7C26RWNBX0CTPK&from=product-detail&storeID=books','nsPrefix':'ns_WNDTWR7C26RWNBX0CTPK_27015_2_','path':'encodeURI('&originalURI=' + window.location.pathname)','title':'Tell Us About a Lower Price'};

return {'trigger':'ns_WNDTWR7C26RWNBX0CTPK_27015_1_hmd_pricing_feedback_trigger_product-detail','destination':'/gp/pdp/pf/pricingFeedbackForm.html/ref=_pfdpb/147-2279433-2379714?ie=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&ASIN=1788621115&PREFIX=ns_WNDTWR7C26RWNBX0CTPK_27015_2_&WDG=book_display_on_website&dpRequestId=WNDTWR7C26RWNBX0CTPK&from=product-detail&storeID=booksencodeURI('&originalURI=' + window.location.pathname)','url':'/gp/pdp/pf/pricingFeedbackForm.html/ref=_pfdpb/147-2279433-2379714?ie=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&ASIN=1788621115&PREFIX=ns_WNDTWR7C26RWNBX0CTPK_27015_2_&WDG=book_display_on_website&dpRequestId=WNDTWR7C26RWNBX0CTPK&from=product-detail&storeID=books','nsPrefix':'ns_WNDTWR7C26RWNBX0CTPK_27015_2_','path':'encodeURI('&originalURI=' + window.location.pathname)','title':'Tell Us About a Lower Price'};

Would you like to tell us about a lower price?If you are a seller for this product, would you like to suggest updates through seller support?

Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn

This Linear Regression in Machine Learning video will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it ...

KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Beginners | Simplilearn

This KNN Algorithm tutorial (K-Nearest Neighbor Classification Algorithm tutorial) will help you understand what is KNN, why do we need KNN, how do we ...

The 7 Steps of Machine Learning

How can we tell if a drink is beer or wine? Machine learning, of course! In this episode of Cloud AI Adventures, Yufeng walks through the 7 steps involved in ...

What is machine learning and how to learn it ?

Machine learning is just to give trained data to a program and get better result for complex problems. It is very close to data ..

What algorithm to choose and how to apply it to your product?

Machine learning and algorithms are two concepts and words that are thrown around a lot these days, but very few actually know how to apply machine learning ...

Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn

This Naive Bayes Classifier tutorial video will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional ...

How to Make a Prediction - Intro to Deep Learning #1

Welcome to Intro to Deep Learning! This course is for anyone who wants to become a deep learning engineer. I'll take you from the very basics of deep learning ...

Python Machine Learning Tutorial | Machine Learning Algorithms | Python Training | Edureka

Python Training : ) This Edureka Python tutorial (Python Tutorial Blog: gives an introduction to Machine .

Training a machine learning model with scikit-learn

Now that we're familiar with the famous iris dataset, let's actually use a classification model in scikit-learn to predict the species of an iris! We'll learn how the ...

A.I. Experiments: Visualizing High-Dimensional Space

Check out to learn more. This experiment helps visualize what's happening in machine learning. It allows coders to see and explore ..