Trending Now

Fostering Cyber Awareness: A Must for Modern Workplaces
The 7 QC Tools for Quality Management
What is one characteristic of an effective Agile Team?
Agile Scrum Foundation: Your First Step Towards Agile Mastery
If a team insists that big Stories cannot be split into smaller ones, how would the Scrum Master coach them to do otherwise?
According to SAFe Principle #10, what should the Enterprise do when markets and customers demand change?
If the distance between the arrival and departure curves on a team's cumulative flow diagram is growing apart, what is likely happening?
How does SAFe recommend using a second operating system to deliver value?
What is the purpose of the Large Solution Level in SAFe?
Why is it important to decouple deployment from release?
Why is the program predictability measure the primary Metric used during the quantitative measurement part of the Inspect and Adapt event?
How can trust be gained between the business and development?
Inspect and Adapt events occur at which two SAFe levels? (Choose Two)
What is the purpose of the retrospective held during an Inspect and Adapt event?
What should be the first step a team should take to feed potential problems into the Problem-Solving workshop?
What is the output of an Inspect and Adapt event?
Lee is a developer on the team. At every daily stand-up Lee reports, "Yesterday, I worked on indexing. Today, I will work on indexing. No impediments."
When is collaboration with System Architects and the Systems Team likely to have the greatest impact on Solution development?
How is team performance calculated in SAFe?
What is the purpose of the scrum of scrums meeting during PI Planning?
Which statement is true about batch size, lead time, and utilization?
During Iteration planning, the Product Owner introduces multiple new Stories to the team.
What is one outcome of an integration point?
What are two ways to develop T-shaped skills? (Choose two.)
What is one way a Scrum Master leads the team's efforts for relentless improvement?
An Agile Team decides they want to use pair programming in future Iterations. Where should this be captured?
What is the purpose of the fishbone diagram?
How is average lead time measured in a Kanban system?
What is one problem with phase-gate Milestones?
What is a benefit of an Agile Release Train that has both cadence and synchronization?
Three teams are working on the same Feature. Team A is a complicated subsystem team, and Teams B and C are stream-aligned teams.
ITIL 4 Foundation in Japan: Career Insights, Salary Trends, and Top Companies
Top Governing Bodies Certifications for Change Management Training
How are the Business Analysts Ruling The Healthcare Industry?
The Role of the ITIL 4 Service Value System in Modern ITSM
Comprehensive Guide to International SEO: Strategy, Implementation, and Best Practices
The Power of Header Tags in SEO - Best Practices and Real-World Impact
Optimizing URL Structures: Insights from My Journey in SEO
The Ultimate 2024 On-Page SEO Checklist: 100+ Points to Boost Your Website's Rankings
Understanding the Importance of Meta Descriptions
Embracing Change and Uncertainty in Projects: Insights from PMBOK's Latest Guide
Agile vs SAFe: Comparison Between Both
Continuous Integration & Continuous Deployment in Agile
Mastering Title Tags for SEO: A Deep Dive into Optimization Techniques
The 5 Pillars of Site Reliability Engineering
Future Of DevOps Engineering in 2024
Beyond the Paycheck: The Rise of Worker-Centric Cultures in Global Industries
What is the primary measurement during Inspect and Adapt?
Which statement is true about refactoring code?
A team integrates and tests the Stories on the last day of the Iteration. This has become a pattern for the last three Iterations.
Which two events provide opportunities for the team to collaborate? (Choose two.)
Why are phase-gate Milestones problematic?
Navigating Project Complexity: Strategies from the PMBOK 7th Edition
How ITIL 4 Enhances Digital Transformation Strategies: The Key to Modernizing IT Infrastructure
Streamlining Vaccine Development during a Global Health Crisis – An Imaginary PRINCE2 Case Study
Which two timestamps are required at minimum to measure lead time by using a Team Kanban board? (Choose two.)
Global Talent, Local Impact: Building Capabilities Across Borders
Introductory Guide to Agile Project Management
How to Start Lean Six Sigma Yellow Belt Certification Journey?
12 Project Management Principles for Project Success
A Beginner's Guide to Site Reliability Engineering
Agile vs. DevOps: Difference and Relation
What is Agile Testing for Projects? - Best Practices & Benefits
What is Agile: History, Definition, and Meaning
The Agile Way of Thinking with Examples
Product Owner Responsibilities and Roles
CSM vs. SSM: Which Scrum Master Certification is Better?
Agile Scrum Product Owner Roles & Responsibilities
Top 7 Project Management Certifications to Level Up Your IT Career
Guide to Scrum Master Career Path in 2024
Scrum Master Certification Exam Preparation Guide
Agile Scrum Best Practices for Efficient Workflow
Advantages of Certified Scrum Master
How to Get CSPO Certification?
Top 7 Ethical Hacking Tools in 2024
Ethical Hackers Salary Worldwide 2024!
The Complete Ethical Hacking Guide 2024
SRE vs DevOps: Key Differences Between Them
Everything about CISSP Certification
How to Pass the CISSP Certification?
What is one way a Scrum Master can gain the confidence of a stakeholder?
The ART stakeholders are concerned. What should be done?
What does a Scrum Master support in order to help the team improve and take responsibility for their actions?
What are two characteristics of teams that fear conflict?
What goes into the Portfolio Backlog?
What are three opportunities for creating collaboration on a team? 
The purpose of Continuous Integration is to deliver what?
Which of the four SAFe Core Values is an enabler of trust?
What is one requirement for achieving Continuous Deployment?
When should centralized decision-making be used?
What is a Product Owner (PO) anti-pattern in Iteration planning?
How are the program risks, that have been identified during PI Planning, categorized?
The work within one state of a team's Kanban board is being completed at varying times, sometimes running faster and sometimes slower than the next state. What could resolve this issue?
What is a good source of guidance when creating an improvement roadmap that improves the teams technical practices?
A team consistently receives defect reports from production even though each Story is thoroughly tested. What is the first step to solve this problem?
What are two benefits of applying cadence? (Choose two.)
Which statement is true about work in process (WIP)?
What are relationships within a highly collaborative team based on?
A Scrum Master is frustrated that her team finds no value during Iteration retrospectives, and the team has asked that she cancel all future ones. Which two specific anti-patterns are most likely present within the team’s retrospectives? (Choose two.)
What are two purposes of the scrum of scrums meeting? (Choose two.)
Real-World Machine Learning & AI Examples

Machine Learning and AI Real-World Examples

Picture of Stefan Joseph
Stefan Joseph
Stefan Joseph is a seasoned Development and Testing and Data & Analytics, expert with 15 years' experience. He is proficient in Development, Testing and Analytical excellence, dedicated to driving data-driven insights and innovation.

Machine Learning and Artificial Intelligence have effortlessly supported every level of organization and business to increase their productivity. The collaboration of these stupendous entities will boost labor productivity by 40% or more by 2035, according to a report by Accenture. The image below shows how AI will impact every industry’s profitability in the future. In this blog, we’ll discuss how industries are leveraging Machine Learning and Artificial Intelligence to grow better in their specific domains.

How AI will be impact on profits by industry?

Image Source:

Rate of AI/ML Technologies Adoption

Since the pandemic hit the market, it slowed the supply chain, and in every other ground of business, remote working has evolved. On top of that, indulging in less human-interacted jobs has also evolved, and that was when business leaders decided to inculcate AI/ML technologies. On what grounds have businesses integrated Machine Learning projects with AI to succeed in their genres?

How fast organizations are adopting AI in their businesses

Image Source: Polestarllp

The next image will show Annual growth rates by 2035 of gross value added (a close approximation of GDP), comparing baseline growth to an artificial intelligence scenario where AI has been absorbed into a sector’s economic processes.

2035 growth rates: baseline vs. AI integrated economic processes scenario

Image Source:

Brands using Machine Learning Technologies

1. Content Discovery- Pinterest

Pinterest is one of the most versatile platforms with a collection of content; the best part is that it is visually appealing. In 2015, Pinterest acquired Kosei, one of the best minds in Machine Learning and data science. Pinterest is famous for its curated content, and Kosei is boosting the whole idea of content discovery and recommendation algorithms. Nowadays, Machine Learning is involved in almost everything at Pinterest, like keeping out spam, helping you discover content, making money from ads, and keeping more people interested in email newsletters. It’s pretty awesome!

Content recommendations and curation on the Pinterest platform

2. Facebook Chatbot

Facebook Messenger has become something of an experimental testing laboratory for chatbots, and it is one of the most used social media messaging platforms in the world. Any developer can make and send a chatbot for Facebook Messenger. This allows companies, even small startups with limited tech capabilities, to use chatbots for customer service and to keep customers engaged.

But that’s not the only way Facebook is using Machine Learning. They’re also applying AI to filter out spam and low-quality content. Additionally, they’re exploring computer vision algorithms to help visually impaired individuals “read” images.

AI chatbots in Facebook Messenger

Image Source:

3. Smart Sales with Hubspot

Just like Pinterest, Hubspot has also entered into an acquisition with a renowned Machine Learning firm Kemvi. Predictive lead scoring is just one of the crucial potential applications of AI and Machine Learning. With Kemvi’s DeepGraph Machine Learning and natural language processing tech, Hubspot is excelling in its internal content management system.

Image Source:

Kemvi’s DeepGraph Machine Learning and natural language processing tech

Image Source:

4. Salesforce’s CRM

Salesforce is a big player in the tech world, especially in helping businesses manage their relationships with customers. One of the tough things for digital marketers is predicting and scoring leads. That’s why Salesforce is investing a lot in its own Einstein Machine Learning tech to tackle this challenge.

Salesforce Einstein enables businesses using Salesforce’s CRM software to examine all aspects of a customer’s relationship, starting from the first interaction to ongoing engagement points. This helps create more detailed customer profiles and pinpoint important moments in the sales process. As a result, businesses can achieve more thorough lead scoring, provide better customer service (leading to happier customers), and discover more opportunities.

Salesforce’s CRM

Image Source:

5. Google Neural Network

Neural Network by Google is one of the most significant platforms for tech nerds. 

One of the standout advancements in Google’s neural network research is the DeepMind network, often called the “machine that dreams.” This is the same network responsible for generating those widely discussed psychedelic images.

Google states that its research covers “virtually all aspects of Machine Learning.” This broad exploration is expected to bring about exciting progress in what Google terms “classical algorithms” and other applications, such as natural language processing, speech translation, and systems for predicting and ranking search results.

Google Neural Network architecture

Image Source: Blog.Research.Google

Future of Machine Learning

Machine Learning and Artificial Intelligence are the most researched and popular technologies that the entire globe is running after. Hash Studioz Technologies indicates that the global market size for ML is projected to hit $209.91 billion by 2029, experiencing a notable Compound Annual Growth Rate (CAGR) of 38.8%.

Machine learning, a subset of artificial intelligence, has an impact that ranges from forecasting the spread of diseases like COVID-19 to supporting advanced cancer research. Therefore, it is becoming almost impossible for everyone to imagine a fully functioning world without AL and ML.

Trends in Machine Learning

Trends in Machine Learning in 2024

1. The Big Model Creation

An all-purpose model with the ability to simultaneously multitask is expected to emerge in the next few years. You actually won’t need to understand the framework’s applications, while the model will be trained in a plethora of domains that depend on what your organization or business is demanding. A well-designed quantum processor will certainly boost ML capabilities. 

2. The Power of Reinforcement Learning

Reinforcement learning (RL) will support companies in making smart business decisions in a dynamic setting without being specifically taught. RL is expected to provide us with new ways to deal with unforeseen circumstances and therefore, enhance the predictive modeling in a better way. Within the next few years, we will likely see several breakthroughs in RL in industries like economics, biology, and astronomy.

3. The Quantum Computing Effect

Experts in the industry are planning to integrate a quantum computing-based approach to optimize Machine Learning speed. It will boost simultaneous multi-stage operations such as reducing execution times in high-dimensional vector processing. There are currently no such models available on the market, but tech giants are working hard to develop them. It can be difficult to predict Machine Learning’s future due to uncertainty.

4. No-Code Environment

Machine Learning technology when collaborated with open-source frameworks like TensorFlow, sci-kit-learn, Caffe, and Torch will automatically reduce coding efforts for data teams. This will be a phenomenal aspect in the world of non-programmers who can refer to online packages for addressing coding scenarios. Additionally, automated Machine Learning will improve results and analysis quality.

5. Distributed ML Portability

With the growth of databases and cloud storage, data teams seek greater flexibility in dataset usage. Advancements in distributed Machine Learning will eliminate the need to build algorithms from scratch for each platform, enabling seamless integration of work into new systems. This suggests a future where ML tools run effortlessly on diverse platforms, eliminating the need for toolkit switches, with discussions among experts focusing on abstraction layers to facilitate this technological leap.

Industries Disrupted by Machine Learning

Machine Learning is poised to disrupt even more industries in the future. Here are a few examples:

  • Energy: Machine Learning can be used to optimize energy production and distribution, improve energy efficiency, and develop new renewable energy sources.

  • Construction: Machine Learning can be used to automate tasks, improve safety, and reduce costs.

  • Logistics: Machine Learning can be used to optimize transportation routes, improve fleet management, and reduce delivery times.

  • Media: Machine Learning can be used to personalize content recommendations, develop new forms of interactive media, and automate content creation.

  • Government: Machine Learning can be used to improve public safety, streamline government services, and make better decisions.


With the latest concepts and tactics, tech experts are leveraging the best utilities of Machine Learning. Industries are already exercising superior Machine Learning projects by addressing the pain points of the customers and thereby, growing better. Machine Learning is a classic asset of Artificial Intelligence and together they’re bringing top-notch revolution in the world ranging from healthcare to sports. If you’re the one who’s looking for a career in Machine Learning and bringing substantial changes to the world, this is the time!

Leave a Reply

Your email address will not be published. Required fields are marked *

Popular Courses

Follow us









Subscribe us