Democratizing Artificial Intelligence with UiPath E-Book

Fanny Ip

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 310

Veröffentlichungsjahr: 2022

Beschreibung

Artificial intelligence (AI) enables enterprises to optimize business processes that are probabilistic, highly variable, and require cognitive abilities with unstructured data. Many believe there is a steep learning curve with AI, however, the goal of our book is to lower the barrier to using AI. This practical guide to AI with UiPath will help RPA developers and tech-savvy business users learn how to incorporate cognitive abilities into business process optimization. With the hands-on approach of this book, you'll quickly be on your way to implementing cognitive automation to solve everyday business problems.
Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will help you understand the power of AI and give you an overview of the relevant out-of-the-box models. You’ll learn about cognitive AI in the context of RPA, the basics of machine learning, and how to apply cognitive automation within the development lifecycle. You’ll then put your skills to test by building three use cases with UiPath Document Understanding, UiPath AI Center, and Druid.
By the end of this AI book, you'll be able to build UiPath automations with the cognitive capabilities of intelligent document processing, machine learning, and chatbots, while understanding the development lifecycle.

Leseprobe

Democratizing Artificial Intelligence with UiPath

Expand automation in your organization to achieve operational efficiency and high performance

Fanny Ip

Jeremiah Crowley

BIRMINGHAM—MUMBAI

Democratizing Artificial Intelligence with UiPath

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Foreword

The mere mention of the term AI creates a cascade of reactions. We have all heard about aspects of the technology, some real, some prospective, and this has evoked a spectrum of views. Some believe the application of this capability is a giant leap forward to help solve some of society's most perplexing problems. Others are certain that it is opening the door to the marginalization of humanity and the subjugation of people to the whims of software. Regardless of your viewpoint, AI is rapidly becoming a widely used technology to help solve problems, both operationally challenging and predictively based, where large amounts of information are used to spot and specify trends and create valuable outputs.

There are those of us who have been involved in this area of technology for years, who have a growing number of those pesky gray hairs appearing on our heads and have seen several new capabilities appear and grow to become part of the new normal. From the perspective of time, these new capabilities tend to follow a clear, general trend. A new technology is developed and applied in a few cases with highly impactful results. As more people learn of this, they begin to imagine broader adoption with the promise of a quantum leap forward in solution design. Whether as a result of over-enthusiastic ambition or a lack of application expertise, there comes a bit of a resetting of expectations, a real-world reset if you will. However, through these initial phases of adoption, there is a growing number of dedicated professionals who are learning the skills necessary for both envisioning the use of the new technology as well as seeing its application in a broad and growing domain that expands the impact of this technology. This is typically when the rapid and steady growth of adoption takes flight.

This cycle of Introduction – Over-hyped use or impact – Resetting of expectations – Democratization – Explosive growth is a well-known trend. The democratization phase is one of the most impactful. When more practitioners understand and are skilled in the use of the technology, they can immediately begin to see possible applications in real-world situations. This phase accomplishes two things: one, it dramatically increases the skill base of practitioners, and two, it opens the aperture of deployment use cases and furthers the adoption of the new technology.

In this work, our authors have set out not only how to begin understanding and deploying this technology, but also to define and stratify the various levels of AI and clearly instruct the reader on how to use it. For the inquisitive reader, the road to expertise begins with the first steps and this book will guide you through those first steps. I encourage you to read this, understand and imagine the possible uses of AI, and let your own curiosity take you forward.

Foreword by Tom Torlone - Automation thought leader who has helped many companies scale their transformation programs.

Contributors

About the authors

Fanny Ip is a thought leader in automation, business transformation, and innovation. Currently, Fanny is the VP of the automation consulting innovation office at UiPath and leads a team of experienced management consultants in developing field-tested solutions for customers.

Before joining UiPath, Fanny co-led McKinsey's automation practice in North America and developed automation strategies for CXOs to deliver revenue growth and achieve a bottom-line impact. Before automation, Fanny has also led business transformation projects at PwC and Deloitte.

Fanny earned a BA in economics from the University of Chicago and an MBA from the Anderson School of Management at UCLA. Originally from Hong Kong, Fanny resides in Los Angeles with her family.

Jeremiah Crowley is a multi-disciplined developer with a wide technical and operational lens. His initial passion for software development and implementation led him to automation and process efficiency. He enjoys working on and solving problems, irrespective of their size and the technology involved.

Jeremiah received his BA in computer science from New York University. His work history includes the likes of EY and McKinsey & Company, where he assumed roles within their automation and service operations practices. He is currently a director within UiPath's automation consulting innovation office, assisting organizations in driving adoption and scaling automation by managing digital transformation initiatives and developing hyper-automation frameworks and methodologies.

About the reviewer

Azim Zicar is a low-code platform subject matter expert who helps businesses achieve digital transformation using RPA and Power Platform technologies. He is an experienced lead RPA developer certified by Blue Prism as an accredited professional developer and by UiPath as an advanced RPA developer. Azim is also a Microsoft Certified Power Platform Solution Architect Expert and has enabled multiple businesses to maximize and correctly utilize all of the different components of the platform. As a TOGAF certified enterprise architect, he can help you shape both business and technical strategies. He contributes to the community via public talks, workshops, and articles, all of which you can find on his website: Zicar Consultancy Ltd.

Table of Contents

Preface

Section 1: The Basics

Chapter 1: Understanding Essential Artificial Intelligence Basics for RPA Developers

Understanding key AI concepts

Differentiating between artificial intelligence, machine learning, and deep learning

Appreciating the relevance of supervised learning, unsupervised learning, and reinforcement learning in AI

Practical tips

Understanding cognitive automation

Understanding the expanded roles the RPA developer plays in the cognitive automation life cycle

Understanding the final output of the cognitive automation life cycle and the RPA life cycle

Practical tips

Exploring relevant OOTB models for RPA developers

The commonly used OOTB models

Practical tips

Summary

Further reading

Chapter 2: Bridging the Gap between RPA and Cognitive Automation

Understanding the spectrum of office work

Data collection

Data processing

Applying expertise

Stakeholder interactions

Exploring the gap between RPA and cognitive automation

Practical tips

Designing human-machine collaboration with cognitive automation

Demonstrating human-machine collaboration with examples

Applying differences between how humans and machines work in cognitive automation design

Practical tips

Summary

Chapter 3: Understanding the UiPath Platform in the Cognitive Automation Life Cycle

Understanding the critical success criteria in choosing a cognitive automation platform

Guiding principles of a versatile automation platform

Design principles for human-machine collaboration in cognitive automation

Introducing UiPath's end-to-end cognitive automation platform

Discover pillar – discovering, evaluating, and managing automation use case pipelines

Build pillar – developing automations

Manage pillar – managing, deploying, and optimizing automations

Engage pillar – facilitating human-robot collaboration in automations

Run pillar – running automations

Getting to know UiPath Document Understanding

The benefits of UiPath Document Understanding

UiPath Document Understanding technical framework

Getting to know UiPath AI Center

The benefits of UiPath AI Center

UiPath AI Center technical concepts

Getting to know the UiPath chatbot with Druid

Benefits of the UiPath chatbot with Druid

Technical components of the Uipath chatbot with Druid

Summary

Section 2: The Development Life Cycle with AI Center and Document Understanding

Chapter 4: Identifying Cognitive Opportunities

Searching for automation opportunities

The characteristics of an automation opportunity

Identifying target goals

Seeking automation opportunities

Understanding the opportunity

Evaluating opportunities

Prioritizing the pipeline

Looking at the end-to-end process

Probing for cognitive automation

Summary

QnA

Chapter 5: Designing Automation with End User Considerations

Gathering requirements

Gathering the current state

Setting target goals

Designing the solution

Choosing the correct automation type

Designing automation for the best user experience

Summary

Chapter 6: Understanding Your Tools

Technical requirements

Enabling AI Center in the UiPath enterprise trial

Getting started with UiPath Document Understanding

Introducing the Document Understanding framework

Getting started with UiPath AI Center

Using AI Center

Getting started with UiPath Computer Vision

Using Computer Vision

Summary

QnA

Chapter 7: Testing and Refining Development Efforts

Approaching cognitive automation testing

How to test RPA development

How to test cognitive components

Executing cognitive automation testing

Gathering test data

Executing RPA testing

Executing cognitive testing

Executing UAT

Closing the feedback loop

Summary

QnA

Section 3: Building with UiPath Document Understanding, AI Center, and Druid

Chapter 8: Use Case 1 – Receipt Processing with Document Understanding

Technical requirements

Enabling AI Center in the UiPath Enterprise trial

Understanding the current state

Creating the future state design

Building the solution with the Document Understanding framework

Setting up the Document Understanding Process template

Creating the taxonomy

Setting up the digitizer

Setting up the classifier

Setting up the extractor

Setting up the exporter

Testing to ensure stability and improve accuracy

Enabling the Validation Station

Testing with sample receipts

Deploying with the end user experience in mind

Creating the dispatcher

Adding user input prompts

Deploying into production

Summary

Chapter 9: Use Case 2 – Email Classification with AI Center

Technical requirements

Enabling AI Center in UiPath Enterprise trial

Understanding the current state

Creating a future state design

Building a solution with AI Center

Creating an ML skill

Building automation workflows

Testing to ensure stability and improve accuracy

Testing with sample emails

Building a retraining workflow

Deploying with the end-user experience in mind

Deploying the project

Summary

Chapter 10: Use Case 3 – Chatbots with Druid

Technical requirements

Understanding the current state of the use case

Creating a future state design

Building a solution with Druid

Creating a project

Editing the Welcome flow

Creating an automation flow

Building an automation solution

Creating a project

Building Reset_Password and Upgrade_App workflows

Building the Main workflow

Testing to ensure stability and improve accuracy

Publishing the Druid chatbot

Publishing the UiPath automation

Testing the use case

Considerations for production

Summary

Chapter 11: AI Center Advanced Topics

Technical requirements

Enabling AI Center in UiPath Enterprise trial

NER with AI Center

Introducing AI Center's NER models

Custom NER with UiPath

Deploying your own custom models to AI Center

Preparing a Python model for AI Center

Deploying to AI Center

Interacting with UiPath automation

Summary

Other Books You May Enjoy

Preface

Artificial intelligence (AI) enables enterprises to optimize business processes that require cognitive abilities, are probabilistic, and have high variability and unstructured data in terms of documents, images, and voice. RPA developers and tech-savvy business users do not have a high barrier to using AI to solve everyday business problems. This practical guide to AI with UiPath will help AI engineers and RPA developers working with business process optimization to put their knowledge to work. The book takes a hands-on approach to implementing automation in business use cases in no time.

Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book begins by helping you understand the power of AI and gives you an overview of leveraging relevant out-of-the-box models. You'll learn about cognitive AI in the context of RPA, the basics of machine learning, and how to apply cognitive automation within the development life cycle. You'll then put your skills to the test by building three use cases with UiPath Document Understanding, UiPath AI Center, and Druid.

By the end of this AI book, you'll be able to build UiPath automation with the cognitive capabilities of Document Understanding and AI Center while understanding the development life cycle.

Who this book is for

AI engineers and RPA developers who want to upskill and deploy out-of-the-box models using UiPath's AI capabilities will find this guide useful. A basic understanding of robotic process automation and machine learning will be beneficial, but not mandatory, to get started with this UiPath book.

What this book covers

Chapter 1, Understanding Essential Artificial Intelligence Basics for RPA Developers, will cover the key AI concepts that are relevant in your daily work as an RPA developer.

Chapter 2, Bridging the Gap between RPA and Cognitive Automation, will explore in detail the benefits of adding cognitive automation to your Robotic Process Automation (RPA) toolkit.

Chapter 3, Understanding the UiPath Platform in the Cognitive Automation Life Cycle, will explore the UiPath platform to appreciate how using this platform can help you accelerate and amplify cognitive automation.

Chapter 4, Identifying Cognitive Opportunities, will focus on how to search for cognitive opportunities, and qualify automation opportunities, to ensure the opportunity is fit for cognitive automation.

Chapter 5, Designing Automation with End User Considerations, will focus on how to gather user goals and requirements, and learn how to incorporate these goals into the design of future state automation.

Chapter 6, Understanding Your Tools, will review the activities of Document Understanding, AI Center, and Computer Vision, while reviewing the Document Understanding framework and learning about the out-of-the-box models available with AI Center.

Chapter 7, Testing and Refining Development Efforts, will review how to prepare test data and test cases, and also how to train and increase model accuracy by closing the feedback loop with human validation and UiPath's built-in validation features.

Chapter 8, Use Case 1 – Receipt Processing with Document Understanding, will demonstrate how to build cognitive automation that can interpret the images of receipts using UiPath Document Understanding.

Chapter 9, Use Case 2 – Email Classification with AI Center, will demonstrate how to build cognitive automation that can classify the text of emails using UiPath AI Center.

Chapter 10, Use Case 3 – Chatbots with Druid, will demonstrate how to build a chatbot with Druid that can interact with UiPath automation.

Chapter 11, AI Center Advanced Topics, will cover advanced topics with UiPath AI Center, including Named Entity Recognition and deploying custom ML models.

To get the most out of this book

You will need to have experience building UiPath automation, ideally having completed UiPath's RPA Developer Foundation course. You should be familiar with working in UiPath Studio and UiPath Automation Cloud. You will need a version of UiPath Studio (2021.10+) installed on your computer.

You will also need an Enterprise License of UiPath Automation Cloud to continue with the book. A 60-day trial of UiPath's Enterprise Automation Cloud can be acquired at https://cloud.uipath.com/portal_/register.

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Section 1: The Basics

In this section, RPA developers will learn the relevant AI concepts, understand how cognitive automation works with RPA to increase the potential for automation, and gain an appreciation of the AI strategy and approach within the UiPath platform.

This section comprises the following chapters:

Chapter 1, Understanding Essential Artificial Intelligence Basics for RPA DevelopersChapter 2, Bridging the Gap between RPA and Cognitive AutomationChapter 3, Understanding the UiPath Platform in the Cognitive Automation Life Cycle

Chapter 1: Understanding Essential Artificial Intelligence Basics for RPA Developers

In this chapter, we will cover some key artificial intelligence (AI) concepts that are relevant in your daily work as an RPA developer. We will discover where a robotic process automation (RPA) developer can make the most impact on implementing cognitive automation in RPA use cases without becoming a data scientist. We will also look at real business problems today that are solved by AI.

In this chapter, we will cover the following main topics:

Understanding key AI conceptsUnderstanding cognitive automation Exploring out-of-the-box (OOTB) machine learning (ML) models for RPA developers

By the end of the chapter, you will be equipped with common AI fundamentals, and you will be inspired by real-life examples to help you start thinking about how to apply AI to your potential use cases.

Understanding key AI concepts

You may have come across many terms when you started exploring the topic of AI. We will demystify AI and only present those concepts that are most relevant to you as an RPA developer. Please note that you may come across other material with slightly different definitions based on a different context.

Differentiating between artificial intelligence, machine learning, and deep learning

AI, ML, and deep learning (DL) are related but not the same. The following figure illustrates the hierarchy of these types of learning:

Figure 1.1 – AI, ML, and DL

AI: This is equivalent to giving a machine or a robot the ability to think. It encompasses ML and DL.ML: This refers to how a machine or a robot learns to think through algorithms without explicit programming. ML is a subset of AI.DL: This refers to how an ML algorithm leverages artificial neural networks to mimic learning. DL is a subset of ML.

Next, we will look at three key considerations when choosing between ML and DL. They are listed here:

Data requirement and availabilityComputational powerTraining time

The following figure shows a comparison of ML and DL:

Figure 1.2 – Comparison of ML and DL

In ML, the features of the studied subjects are fed into the algorithms for the machine to learn. We can think of features as us giving hints to the algorithm. This step allows for a smaller dataset, lower computational power, and less training time.

In DL, features are determined by artificial neural networks. It needs to work much harder to figure out the features and patterns to learn. As a result, it requires a large amount of data, high computational power, and a long training time.

Although DL is valuable, it is beyond the reach of most businesses to develop DL models to solve their business problems. Fortunately, many DL models have been pre-trained by companies with the time and budget to make them accessible to a large user base.

The implication of this option means that your role as an RPA developer is not to create these models. You, as the RPA developer, are the trainer of these models. It is important to understand the role of training in AI.

Appreciating the relevance of supervised learning, unsupervised learning, and reinforcement learning in AI

As we learned in the previous section, AI is about training a machine or a robot to think. Just like a human being, a robot needs to learn. There are three different types of learning for a robot.

The following figure gives some analogies for supervised learning, unsupervised learning, and reinforced learning:

Figure 1.3 – Supervised learning, unsupervised learning, and reinforcement learning analogies

The following list explains the various analogies:

Supervised learning: This is based on past data, and the trainer specifies the inputs to predict future outcomes. This type of training is analogous to an instructor-led training course. It requires the trainer to supervise the student or the model to achieve the desired learning outcome. Classification and regression are types of supervised learning methods: Classification refers to the process of categorizing a given set of data into classes. For example, a set of pictures of different animals are fed into the ML model. Each picture is labeled with an animal name. The ML model is trained to identify animals from an image. Regression helps in the prediction of a continuous variable. For example, a profit prediction ML model is an example of a regression model. Training data consisting of R and D, marketing, and administrative spending, geographic location, and profit is fed into the model. The ML model predicts the profit. Unsupervised learning: This relies on an algorithm to identify unknown patterns from data. This type of training is analogous to a self-study course. It requires the students or the model to synthesize the information to achieve the desired learning outcome. Clustering is a type of unsupervised learning method:Clustering refers to the method used to find similarity and relationship patterns among training datasets, and then cluster those datasets into groups with similarities based on features. For example, the clustering technique is commonly used in market segmentation. The ML model looks at features such as sex, age, race, and geographic location to group customer groups into segments to better understand their buying habits. Reinforced learning: This uses a reward-and-punishment system to learn. There is no training data or trainer. The algorithm is improved over time based on feedback or reward and punishment. This type of training is analogous to on-the-job training. If the worker is doing the job well, the worker gains a pay raise or promotion. If the worker is performing poorly, the worker receives no raise or promotion. This is commonly used when no data or specific expertise is available.

Practical tips

AI platform providers have a mission to make AI accessible. Part of that mission is striving to develop product features to overcome the complex concepts of AI. Specifically, these are some notable democratization efforts in AI:

Increased availability of pre-trained models to accelerate the time to resultSimplification of the technical complexity of the ML training life cycle

We presented the key AI concepts in an easily digestible format. This overview prepares you to pick up an AI platform such as UiPath quickly. You will build, deploy, and maintain your first AI+RPA use cases in no time. You no longer need to spend years mastering AI to build a model from scratch. Instead, you are the trainer of the robots, teaching different skills that they need to master. Most importantly, you have tools that do the most complex tasks for you.

Now that you have a good understanding of the key AI concepts, let's explore cognitive automation, which is the combination of AI and RPA.

Understanding cognitive automation

Cognitive automation or intelligent process automation (IPA) refers to the use of AI and RPA together. It provides the machine or the robot with the brain (AI) and the limbs (RPA).

Although the general software development life cycle (SDLC) looks the same at a high level for RPA development and cognitive automation development, there are two important differences:

The role of the RPA developer across the SDLCThe final output of the RPA and cognitive automation life cycles

Let's now take a look at these differences in detail.

Understanding the expanded roles the RPA developer plays in the cognitive automation life cycle

An RPA developer plays expanded roles in the cognitive automation SDLC. A detailed comparison between a representative RPA SDLC and a representative cognitive automation SDLC is given in the following figure:

Figure 1.4 – Differences in RPA developer roles in the RPA and cognitive automation SDLCs

In the RPA SDLC, an RPA developer is like a traditional developer for any other software package. In this, the typical sequence of the process is as follows:

The business analyst collects the end-to-end business requirements of a business workflow detailing inputs, process steps, and output.The RPA developer codes the RPA workflow and tests the code.The business user conducts a user-acceptance test of the RPA robot. Finally, the RPA developer creates a package to deploy to the production environment.Post-production, the administrator manages the operations of the RPA bots.The RPA developer updates the code if the business user suggests enhancements or reports bugs.

The RPA developer plays a heavy role in selected steps of the RPA SDLC (build, deploy, and improve) by converting business requirements into RPA language.

In the cognitive automation SDLC, the RPA developer has a role in almost every step, which is described as follows:

The RPA developer collects data-specific requirements to prepare for ML model training/re-training.The RPA developer does not usually build the ML model. Instead, the RPA developer either uses the ML model developed by the data scientist or uses an available OOTB model. The RPA developer prepares the datasets for training and evaluation to train/re-train the ML model according to the specific use cases.When the training result is acceptable, the RPA developer creates the ML package to deploy to the production environment.The ML skills are then available for the RPA developer to plug and play in any RPA workflow.Post-production, the administrator manages the operations of the RPA bots and the ML skills.The RPA developer continues to re-train the model with new data points to improve the model.

In cognitive automation, an RPA developer plays a broader role across the SDLC as a trainer and a data steward.

Understanding the final output of the cognitive automation life cycle and the RPA life cycle

Another important distinction between RPA and cognitive automation is related to the characteristics of the final output produced. RPA configures RPA bots. Cognitive automation develops ML skills that are leveraged by the RPA bot. The following figure illustrates the differences in the expectations of an RPA bot and an ML skill in initial deployment to the stakeholders:

Figure 1.5 – Expectations of an RPA bot and an ML skill in the initial deployment

An RPA robot performs according to a set of rules set out by the RPA developer. The result is black and white. Only the correctly coded robot is deployed into production. The output of the cognitive automation life cycle is a trained ML skill combined with an RPA workflow. The ML skill is trained up to the acceptable threshold of confidence to be deployed into production. In almost all cases, the ML skill is not 100% correct when it is first deployed. The ML skill is expected to improve over time.

Practical tips

Businesses have seen the power and reap the benefits of automation through RPA. However, RPA has its limitations. RPA can only automate rule-based tasks, thus limiting the scope of a process it can automate. In addition, rule-based tasks are usually lower-value work. To move up the value chain, combining AI is essential for businesses to maintain a competitive advantage. Here are some of the key takeaways to bring to your leadership:

Technology companies have simplified AI technologies to make them accessible for consumption. AI is no longer a tool that only data scientists can leverage.The existing RPA team can start incorporating AI without needing heavy investments in springing up a new team. There are impactful cognitive automation use cases throughout the organization. It is now time to give the machine or the robot a brain.

Now that you have a good understanding of cognitive automation, let's explore the most commonly used OOTB models that you can try as a beginner in AI.

Exploring relevant OOTB models for RPA developers

You have options when it comes to ML models. There are widely available OOTB models that you can use by re-training with your data. You can develop your ML models from scratch. Lastly, you can collaborate with the data scientists in your company on custom-built ML models.

In this book, we will provide tips on how you engage with these options. To begin, we recommend you start with the OOTB models. We will give you an overview of the most commonly used OOTB models in this section.

The commonly used OOTB models

OOTB ML models apply to a wide variety of use cases. They are pre-trained with a large amount of data. Some OOTB models can be retrained with your specific dataset, while others are not retrainable. Most automation platforms now include OOTB models. Selecting the right OOTB models can save you time and accelerate your project. The following figure illustrates the different categories of the OOTB models:

Figure 1.6 – OOTB ML models by category

These OOTB ML models convert various forms of unstructured data into a usable format. The usage of these models reduces reliance on humans to spend hours reading, processing, comprehending, and analyzing unstructured documents. Unstructured documents can come in the form of images, language, tabular text, and documents.

Let's take a closer look at each of these models:

Image analysis: There are two image analysis OOTB models. The following figure summarizes the key characteristics of the two models:

Figure 1.7 – Image analysis OOTB models

These two OOTB image analysis models are useful for many use cases that involve analyzing an image to determine the next steps. For example, the image moderation model is often used in social media feed moderation. The OOTB image moderation model reviews millions of images and flags images that may be problematic for humans to verify.

Language translation: As the name suggests, language translation replaces the tedious work of translation from one language to another. The following figure summarizes the key characteristics of the model:

Figure 1.8 – Language translation OOTB models

This ML skill can be used in a variety of use cases and is commonly used in customer support. For example, many chatbots are powered by an OOTB language translation model to handle inquiries in different languages.

Language comprehension: Language comprehension is complex. It refers to the ability to extract meaning from text, just like a human. The following figure summarizes the key characteristics of the three available models:

Figure 1.9 – Language comprehension OOTB models

Language comprehension ML models can mimic the thinking of a human and make inferences. They have widespread practical usage. For example, the semantic similarity OOTB model provides recommendations based on preferences indicated by the users. The question answering OOTB model is often used as a basis to build an automated frequently asked questions (FAQ) database. Finally, the text summarization OOTB model draws insights from books and articles.

Language analysis: Language analysis refers to the skill of drawing meaning from text. It enables a machine or a robot to understand sentences and paragraphs. The following figure summarizes the key characteristics of the three kinds of models:

Figure 1.10 – Language analysis OOTB models

Language analysis ML models know how to draw context and relationships between individual words. They have widespread practical usage. For example, the sentiment analysis OOTB model is often used in managing emails from customers. The model prioritizes negative emails for humans to review. One popular usage of the text classification model is spam email classification. Finally, a named entity recognition model is often used to extract key parts from customer feedback.

Tabular data: Tree-based pipeline optimization tool (TPOT) is a tool to find the best pipeline for your data. The following figure summarizes the key characteristics of the two available models:

Figure 1.11 – Tabular data OOTB models

This OOTB tool automates the most tedious part of pipeline building. In addition, this is an introduction for a beginner to create a custom model.

Documents: Processing documents is time-consuming and tedious. Many businesses spend many hours and a lot in human resources to digitize analog documents and extract structured information from them. The following figure summarizes the key characteristics of the three kinds of models:

Figure 1.12 – Documents OOTB models

There are many documents on OOTB models available to tackle document digitization. They are often pre-trained with a large dataset of the relevant document type. They can be used to accelerate cognitive automation involving documents.

Practical tips

As we learned in this section, there are many OOTB models readily available. They have been widely used and proven to be effective. They are also easy to try. Think of a simple use case that involves AI skills and try your hand at any of the OOTB models mentioned in this section. Practice makes the theory you read in this book come alive.

Summary

In this chapter, you learned about the key AI concepts to start your immersion into AI. In addition, you learned about the power of cognitive automation to extend automation benefits and your role in cognitive automation implementation. Finally, you are now aware of the commonly used OOTB models for you to start hands-on exploration.

In the next chapter, we will dive into exploring the automation spectrum, the available technologies, and a framework to reimagine and solve a business problem with the relevant application of cognitive automation.

Further reading

MIT OpenCourseware – Artificial Intelligence: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/index.htmMcKinsey's An executive's guide to AI: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/An%20executives%20guide%20to%20AI/An-executives-guide-to-AI.ashx

Chapter 2: Bridging the Gap between RPA and Cognitive Automation

In this chapter, we will explore in detail the benefits of cognitive automation over pure robotic process automation (RPA). It is critical to deep dive into the spectrum of office work