Artificial Intelligence (AI) and machine learning are two of the fastest-growing emerging technologies right now. In late 2022, generative AI burst onto the tech scene in the shape of ChatGPT and its antecedents. However, that’s not the first time AI has made a major impact. In fact, the first AI chatbot, Eliza, was around in the 1960s.
Both AI and machine learning do far more than chat and research. AI is embedded in analytics, predictive forecasting, and monitoring for multiple industries. As the use of AI and machine learning expands, the need for professionals with relevant skills is also growing exponentially.
OPIT (Open Institute of Technology) provides top-tier education in various tech fields, including highly respected machine learning and artificial intelligence courses. Let’s take a look at these fascinating technologies and how the right AI machine learning course can elevate your tech career.
Understanding AI and Machine Learning
When you’re searching for courses on artificial intelligence and machine learning, it helps to have a basic definition for both terms. If you already work in the tech industry, you likely work with one or both of these technologies every day. Yet they’re often so embedded within systems or apps that you might not even realize.
AI refers to the computer’s to exhibit behavior that replicates human thought patterns. However, the details of this definition are a little more complex than that. “Computers” can mean anything from a small subsystem to a supercomputer. It can also mean your smartphone or an app. And, by emulating human behavior, experts don’t necessarily mean AI does things exactly like us. Truly “thinking” AI with genuine cognitive abilities is a long way off.
What AI actually does is take things humans can already do – and do it faster and more often. Think about a software DevOps team requiring automated monitoring and testing of code prior to deployment. AI can do this while checking for vulnerabilities and producing relevant, actionable reports. In healthcare, AI uses pattern recognition to diagnose diseases quickly.
Machine learning is a subset of AI. It focuses on using algorithms to consistently and continuously improve pattern recognition for AI that appears to “learn.”
Courses in AI and machine learning are so popular because of the inherent usefulness of these technologies. Learning these skills now is a way to future-proof your tech career.
The Best AI and Machine Learning Courses
Numerous artificial intelligence and machine learning courses cover different topics and niches. You may choose to learn in a classroom setting or remotely. Some courses are short-term, generally covering foundational aspects of AI. Others carry on over several months for a deeper learning experience. Always consider how the course you invest in will impact your career advancement opportunities.
Absolute beginners may benefit from the Coursera IBM Applied Professional Certificate. This course runs entirely online over three months, presuming you can commit to 10 hours a week. Students learn the basics of AI, particularly how it powers IBM’s Watson AI services.
Oxford Online runs a 6-week online AI program course requiring 7-10 hours of commitment a week. This course looks at AI concepts and business cases for implementation and takes a glimpse at the future of AI.
For classroom-based courses on AI and machine learning, prospective students are best placed to contact local educational institutions. Offline courses vary in length, depth, and usefulness, so always check the syllabus and what certification you gain. It’s worth considering how far you’ll have to travel to gain a qualification.
One of the biggest challenges with AI is making it ethical. OPIT addresses that head-on with the MSc in Responsible AI. Learn advanced AI skills while keeping inclusivity and human interest at the heart of every aspect of the syllabus.
OPIT also offers other courses that consider the impact AI has on modern business practice. Undergraduates could consider the BSc in Digital Business, which includes a full Introduction to AI segment. There are also elective topics, including AI-Driven Software Development.
The Structure of AI and Machine Learning Courses
What should you expect from the best courses on AI and machine learning? Each course has a specific length, either in terms of study hours or a set deadline date. Most online courses have a specific intake date to make sure students get the right support at the right time.
Once you start your machine learning and AI course, you can expect a good balance between theory and practical application. For example, OPIT’s master’s degree course starts with foundational theory and critical thinking around ethics in AI. From here, students get to handle complex data sets. They program in Python and learn how to design effective AI-powered data pipelines.
The structure of your course will depend on the focus, but to give you the best foundation, courses may follow a similar pathway to this:
- Basics of AI, including the differences between AI and machine learning
- Discovering applications of AI — these may be general or industry-specific, depending on the nature of your course
- Data collation, analysis, and visualization
- Programming for AI
- Natural language processing (NLP) and natural language generation (NLG)
- Removing or preventing bias in AI training
Some courses will also offer advanced elective programs, such as understanding AI within the sphere of FinOps (financial operations) or business strategy. If you have a particular industry you’re hoping to excel in, look out for courses with topics that could help you further those ambitions.
Online AI and Machine Learning Courses: Flexibility and Accessibility
Choosing one of the best machine learning and AI courses to do online offers more benefits than new skills. Online learning allows you to study in your preferred environment and at your own pace. You just need to make sure you keep an eye on set deadlines.
You’re not distracted by a class full of people, but you still have access to tutors and support. Many open learning institutes have online communities of students. These are great for preventing isolation and gaining advice.
As a tech professional, the ability to set your own study schedule is essential. Online AI and machine learning courses provide flexibility, allowing you to learn as you work. With OPIT’s Master’s Degree in Responsible Artificial Intelligence, you could potentially have an MSc in 12-18 months without taking any time off work.
Key Skills Gained from AI and Machine Learning Courses
When choosing your online course on AI and machine learning, consider the skills you’ll learn. You should expect to cover:
- Data preprocessing
- Data cleansing
- Data visualization and storytelling
- Linear and nonlinear dimensionality reduction
- Manifold learning
- Human-centered AI design
- Language-agnostic AI programming skills
An MSc in AI and machine learning provides specialized skills and knowledge that you can use to address complex AI challenges in just about any industry.
Choosing the Right AI and Machine Learning Course for You
Picking the right AI and machine learning course is simpler when you consider your goals. Do you want a quick upskill and insight into emerging technologies? Or do you want an immersive course that empowers you to take on new career challenges? Most AI and machine learning courses will provide guidance on the type of career students could hope to pursue after completion.
Always look at the syllabus of a course and see if it meets your personal goals. If you’re unsure about any aspects, contact the education provider for more information.
OPIT’S MSc in Responsible Artificial Intelligence: An Overview
If you’ve decided an online AI and machine learning course is for you, as a graduate, an MSc is the natural choice. The next intake for the OPIT MSc in Responsible AI is September 2024, and details on how to apply are online.
What are the benefits of taking this course?
- A fast-track option to gain your master’s degree in just 12 months
- Fully inclusive fees — no hidden charges
- Various scholarship and funding options
- Availability of early-bird discounts
- Access to academic leaders from all over the world
- Education with an EU-accredited institution
Your MSc course covers every aspect of AI you might require for a career in AI and machine learning. Topics start with AI and ethics and quickly move into human-centered design, computer vision, and how AI impacts IoT and automation.
As you move into your final term, you start your MSc thesis, which focuses on AI projects with industrial relevance. There’s also the opportunity to pursue an internship to complement your thesis and gain vital experience.
AI and Machine Learning Courses for a Future-Proof Career
AI is now part of most growing industries, from property and real estate to healthcare and social care. Tech professionals have the opportunity to upskill themselves and move into fields that they have a real passion for. Organizations are looking for and willing to pay high salaries for knowledgeable, qualified AI experts.
Taking the time now to embark on machine learning and AI courses could speed your journey along your chosen career trajectory.
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From the same author
Read the articleSource:
- Agenda Digitale, published on November 25th, 2025
In recent years, the word ” sustainability ” has become a firm fixture in the corporate lexicon. However, simply “doing no harm” is no longer enough: the climate crisis , social inequalities , and the erosion of natural resources require a change of pace. This is where the net-positive paradigm comes in , a model that isn’t content to simply reduce negative impacts, but aims to generate more social and environmental value than is consumed.
This isn’t about philanthropy, nor is it about reputational makeovers: net-positive is a strategic approach that intertwines economics, technology, and corporate culture. Within this framework, digitalization becomes an essential lever, capable of enabling regenerative models through circular platforms and exponential technologies.
Blockchain, AI, and IoT: The Technological Triad of Regeneration
Blockchain, Artificial Intelligence, and the Internet of Things represent the technological triad that makes this paradigm shift possible. Each addresses a critical point in regeneration.
Blockchain guarantees the traceability of material flows and product life cycles, allowing a regenerated dress or a bottle collected at sea to tell their story in a transparent and verifiable way.
Artificial Intelligence optimizes recovery and redistribution chains, predicting supply and demand, reducing waste and improving the efficiency of circular processes .
Finally, IoT enables real-time monitoring, from sensors installed at recycling plants to sharing mobility platforms, returning granular data for quick, informed decisions.
These integrated technologies allow us to move beyond linear vision and enable systems in which value is continuously regenerated.
New business models: from product-as-a-service to incentive tokens
Digital regeneration is n’t limited to the technological dimension; it’s redefining business models. More and more companies are adopting product-as-a-service approaches , transforming goods into services: from technical clothing rentals to pay-per-use for industrial machinery. This approach reduces resource consumption and encourages modular design, designed for reuse.
At the same time, circular marketplaces create ecosystems where materials, components, and products find new life. No longer waste, but input for other production processes. The logic of scarcity is overturned in an economy of regenerated abundance.
To complete the picture, incentive tokens — digital tools that reward virtuous behavior, from collecting plastic from the sea to reusing used clothing — activate global communities and catalyze private capital for regeneration.
Measuring Impact: Integrated Metrics for Net-Positiveness
One of the main obstacles to the widespread adoption of net-positive models is the difficulty of measuring their impact. Traditional profit-focused accounting systems are not enough. They need to be combined with integrated metrics that combine ESG and ROI, such as impact-weighted accounting or innovative indicators like lifetime carbon savings.
In this way, companies can validate the scalability of their models and attract investors who are increasingly attentive to financial returns that go hand in hand with social and environmental returns.
Case studies: RePlanet Energy, RIFO, and Ogyre
Concrete examples demonstrate how the combination of circular platforms and exponential technologies can generate real value. RePlanet Energy has defined its Massive Transformative Purpose as “Enabling Regeneration” and is now providing sustainable energy to Nigerian schools and hospitals, thanks in part to transparent blockchain-based supply chains and the active contribution of employees. RIFO, a Tuscan circular fashion brand, regenerates textile waste into new clothing, supporting local artisans and promoting workplace inclusion, with transparency in the production process as a distinctive feature and driver of loyalty. Ogyre incentivizes fishermen to collect plastic during their fishing trips; the recovered material is digitally tracked and transformed into new products, while the global community participates through tokens and environmental compensation programs.
These cases demonstrate how regeneration and profitability are not contradictory, but can actually feed off each other, strengthening the competitiveness of businesses.
From Net Zero to Net Positive: The Role of Massive Transformative Purpose
The crucial point lies in the distinction between sustainability and regeneration. The former aims for net zero, that is, reducing the impact until it is completely neutralized. The latter goes further, aiming for a net positive, capable of giving back more than it consumes.
This shift in perspective requires a strong Massive Transformative Purpose: an inspiring and shared goal that guides strategic choices, preventing technology from becoming a sterile end. Without this level of intentionality, even the most advanced tools risk turning into gadgets with no impact.
Regenerating business also means regenerating skills to train a new generation of professionals capable not only of using technologies but also of directing them towards regenerative business models. From this perspective, training becomes the first step in a transformation that is simultaneously cultural, economic, and social.
The Regenerative Future: Technology, Skills, and Shared Value
Digital regeneration is not an abstract concept, but a concrete practice already being tested by companies in Europe and around the world. It’s an opportunity for businesses to redefine their role, moving from mere economic operators to drivers of net-positive value for society and the environment.
The combination of blockchain, AI, and IoT with circular product-as-a-service models, marketplaces, and incentive tokens can enable scalable and sustainable regenerative ecosystems. The future of business isn’t just measured in terms of margins, but in the ability to leave the world better than we found it.
Read the articleSource:
- Raconteur, published on November 06th, 2025
Many firms have conducted successful Artificial Intelligence (AI) pilot projects, but scaling them across departments and workflows remains a challenge. Inference costs, data silos, talent gaps and poor alignment with business strategy are just some of the issues that leave organisations trapped in pilot purgatory. This inability to scale successful experiments means AI’s potential for improving enterprise efficiency, decision-making and innovation isn’t fully realised. So what’s the solution?
Although it’s not a magic bullet, an AI operating model is really the foundation for scaling pilot projects up to enterprise-wide deployments. Essentially it’s a structured framework that defines how the organisation develops, deploys and governs AI. By bringing together infrastructure, data, people, and governance in a flexible and secure way, it ensures that AI delivers value at scale while remaining ethical and compliant.
“A successful AI proof-of-concept is like building a single race car that can go fast,” says Professor Yu Xiong, chair of business analytics at the UK-based Surrey Business School. “An efficient AI technology operations model, however, is the entire system – the processes, tools, and team structures – for continuously manufacturing, maintaining, and safely operating an entire fleet of cars.”
But while the importance of this framework is clear, how should enterprises establish and embed it?
“It begins with a clear strategy that defines objectives, desired outcomes, and measurable success criteria, such as model performance, bias detection, and regulatory compliance metrics,” says Professor Azadeh Haratiannezhadi, co-founder of generative AI company Taktify and professor of generative AI in cybersecurity at OPIT – the Open Institute of Technology.
Platforms, tools and MLOps pipelines that enable models to be deployed, monitored and scaled in a safe and efficient way are also essential in practical terms.
“Tools and infrastructure must also be selected with transparency, cost, and governance in mind,” says Efrain Ruh, continental chief technology officer for Europe at Digitate. “Crucially, organisations need to continuously monitor the evolving AI landscape and adapt their models to new capabilities and market offerings.”
An open approach
The most effective AI operating models are also founded on openness, interoperability and modularity. Open source platforms and tools provide greater control over data, deployment environments and costs, for example. These characteristics can help enterprises to avoid vendor lock-in, successfully align AI to business culture and values, and embed it safely into cross-department workflows.
“Modularity and platformisation…avoids building isolated ‘silos’ for each project,” explains professor Xiong. “Instead, it provides a shared, reusable ‘AI platform’ that integrates toolchains for data preparation, model training, deployment, monitoring, and retraining. This drastically improves efficiency and reduces the cost of redundant work.”
A strong data strategy is equally vital for ensuring high-quality performance and reducing bias. Ideally, the AI operating model should be cloud and LLM agnostic too.
“This allows organisations to coordinate and orchestrate AI agents from various sources, whether that’s internal or 3rd party,” says Babak Hodjat, global chief technology officer of AI at Cognizant. “The interoperability also means businesses can adopt an agile iterative process for AI projects that is guided by measuring efficiency, productivity, and quality gains, while guaranteeing trust and safety are built into all elements of design and implementation.”
A robust AI operating model should feature clear objectives for compliance, security and data privacy, as well as accountability structures. Richard Corbridge, chief information officer of Segro, advises organisations to: “Start small with well-scoped pilots that solve real pain points, then bake in repeatable patterns, data contracts, test harnesses, explainability checks and rollback plans, so learning can be scaled without multiplying risk. If you don’t codify how models are approved, deployed, monitored and retired, you won’t get past pilot purgatory.”
Of course, technology alone can’t drive successful AI adoption at scale: the right skills and culture are also essential for embedding AI across the enterprise.
“Multidisciplinary teams that combine technical expertise in AI, security, and governance with deep business knowledge create a foundation for sustainable adoption,” says Professor Haratiannezhadi. “Ongoing training ensures staff acquire advanced AI skills while understanding associated risks and responsibilities.”
Ultimately, an AI operating model is the playbook that enables an enterprise to use AI responsibly and effectively at scale. By drawing together governance, technological infrastructure, cultural change and open collaboration, it supports the shift from isolated experiments to the kind of sustainable AI capability that can drive competitive advantage.
In other words, it’s the foundation for turning ambition into reality, and finally escaping pilot purgatory for good.
-
Go to all authors
Related posts
Source:
- Agenda Digitale, published on November 25th, 2025
In recent years, the word ” sustainability ” has become a firm fixture in the corporate lexicon. However, simply “doing no harm” is no longer enough: the climate crisis , social inequalities , and the erosion of natural resources require a change of pace. This is where the net-positive paradigm comes in , a model that isn’t content to simply reduce negative impacts, but aims to generate more social and environmental value than is consumed.
This isn’t about philanthropy, nor is it about reputational makeovers: net-positive is a strategic approach that intertwines economics, technology, and corporate culture. Within this framework, digitalization becomes an essential lever, capable of enabling regenerative models through circular platforms and exponential technologies.
Blockchain, AI, and IoT: The Technological Triad of Regeneration
Blockchain, Artificial Intelligence, and the Internet of Things represent the technological triad that makes this paradigm shift possible. Each addresses a critical point in regeneration.
Blockchain guarantees the traceability of material flows and product life cycles, allowing a regenerated dress or a bottle collected at sea to tell their story in a transparent and verifiable way.
Artificial Intelligence optimizes recovery and redistribution chains, predicting supply and demand, reducing waste and improving the efficiency of circular processes .
Finally, IoT enables real-time monitoring, from sensors installed at recycling plants to sharing mobility platforms, returning granular data for quick, informed decisions.
These integrated technologies allow us to move beyond linear vision and enable systems in which value is continuously regenerated.
New business models: from product-as-a-service to incentive tokens
Digital regeneration is n’t limited to the technological dimension; it’s redefining business models. More and more companies are adopting product-as-a-service approaches , transforming goods into services: from technical clothing rentals to pay-per-use for industrial machinery. This approach reduces resource consumption and encourages modular design, designed for reuse.
At the same time, circular marketplaces create ecosystems where materials, components, and products find new life. No longer waste, but input for other production processes. The logic of scarcity is overturned in an economy of regenerated abundance.
To complete the picture, incentive tokens — digital tools that reward virtuous behavior, from collecting plastic from the sea to reusing used clothing — activate global communities and catalyze private capital for regeneration.
Measuring Impact: Integrated Metrics for Net-Positiveness
One of the main obstacles to the widespread adoption of net-positive models is the difficulty of measuring their impact. Traditional profit-focused accounting systems are not enough. They need to be combined with integrated metrics that combine ESG and ROI, such as impact-weighted accounting or innovative indicators like lifetime carbon savings.
In this way, companies can validate the scalability of their models and attract investors who are increasingly attentive to financial returns that go hand in hand with social and environmental returns.
Case studies: RePlanet Energy, RIFO, and Ogyre
Concrete examples demonstrate how the combination of circular platforms and exponential technologies can generate real value. RePlanet Energy has defined its Massive Transformative Purpose as “Enabling Regeneration” and is now providing sustainable energy to Nigerian schools and hospitals, thanks in part to transparent blockchain-based supply chains and the active contribution of employees. RIFO, a Tuscan circular fashion brand, regenerates textile waste into new clothing, supporting local artisans and promoting workplace inclusion, with transparency in the production process as a distinctive feature and driver of loyalty. Ogyre incentivizes fishermen to collect plastic during their fishing trips; the recovered material is digitally tracked and transformed into new products, while the global community participates through tokens and environmental compensation programs.
These cases demonstrate how regeneration and profitability are not contradictory, but can actually feed off each other, strengthening the competitiveness of businesses.
From Net Zero to Net Positive: The Role of Massive Transformative Purpose
The crucial point lies in the distinction between sustainability and regeneration. The former aims for net zero, that is, reducing the impact until it is completely neutralized. The latter goes further, aiming for a net positive, capable of giving back more than it consumes.
This shift in perspective requires a strong Massive Transformative Purpose: an inspiring and shared goal that guides strategic choices, preventing technology from becoming a sterile end. Without this level of intentionality, even the most advanced tools risk turning into gadgets with no impact.
Regenerating business also means regenerating skills to train a new generation of professionals capable not only of using technologies but also of directing them towards regenerative business models. From this perspective, training becomes the first step in a transformation that is simultaneously cultural, economic, and social.
The Regenerative Future: Technology, Skills, and Shared Value
Digital regeneration is not an abstract concept, but a concrete practice already being tested by companies in Europe and around the world. It’s an opportunity for businesses to redefine their role, moving from mere economic operators to drivers of net-positive value for society and the environment.
The combination of blockchain, AI, and IoT with circular product-as-a-service models, marketplaces, and incentive tokens can enable scalable and sustainable regenerative ecosystems. The future of business isn’t just measured in terms of margins, but in the ability to leave the world better than we found it.
Source:
- Raconteur, published on November 06th, 2025
Many firms have conducted successful Artificial Intelligence (AI) pilot projects, but scaling them across departments and workflows remains a challenge. Inference costs, data silos, talent gaps and poor alignment with business strategy are just some of the issues that leave organisations trapped in pilot purgatory. This inability to scale successful experiments means AI’s potential for improving enterprise efficiency, decision-making and innovation isn’t fully realised. So what’s the solution?
Although it’s not a magic bullet, an AI operating model is really the foundation for scaling pilot projects up to enterprise-wide deployments. Essentially it’s a structured framework that defines how the organisation develops, deploys and governs AI. By bringing together infrastructure, data, people, and governance in a flexible and secure way, it ensures that AI delivers value at scale while remaining ethical and compliant.
“A successful AI proof-of-concept is like building a single race car that can go fast,” says Professor Yu Xiong, chair of business analytics at the UK-based Surrey Business School. “An efficient AI technology operations model, however, is the entire system – the processes, tools, and team structures – for continuously manufacturing, maintaining, and safely operating an entire fleet of cars.”
But while the importance of this framework is clear, how should enterprises establish and embed it?
“It begins with a clear strategy that defines objectives, desired outcomes, and measurable success criteria, such as model performance, bias detection, and regulatory compliance metrics,” says Professor Azadeh Haratiannezhadi, co-founder of generative AI company Taktify and professor of generative AI in cybersecurity at OPIT – the Open Institute of Technology.
Platforms, tools and MLOps pipelines that enable models to be deployed, monitored and scaled in a safe and efficient way are also essential in practical terms.
“Tools and infrastructure must also be selected with transparency, cost, and governance in mind,” says Efrain Ruh, continental chief technology officer for Europe at Digitate. “Crucially, organisations need to continuously monitor the evolving AI landscape and adapt their models to new capabilities and market offerings.”
An open approach
The most effective AI operating models are also founded on openness, interoperability and modularity. Open source platforms and tools provide greater control over data, deployment environments and costs, for example. These characteristics can help enterprises to avoid vendor lock-in, successfully align AI to business culture and values, and embed it safely into cross-department workflows.
“Modularity and platformisation…avoids building isolated ‘silos’ for each project,” explains professor Xiong. “Instead, it provides a shared, reusable ‘AI platform’ that integrates toolchains for data preparation, model training, deployment, monitoring, and retraining. This drastically improves efficiency and reduces the cost of redundant work.”
A strong data strategy is equally vital for ensuring high-quality performance and reducing bias. Ideally, the AI operating model should be cloud and LLM agnostic too.
“This allows organisations to coordinate and orchestrate AI agents from various sources, whether that’s internal or 3rd party,” says Babak Hodjat, global chief technology officer of AI at Cognizant. “The interoperability also means businesses can adopt an agile iterative process for AI projects that is guided by measuring efficiency, productivity, and quality gains, while guaranteeing trust and safety are built into all elements of design and implementation.”
A robust AI operating model should feature clear objectives for compliance, security and data privacy, as well as accountability structures. Richard Corbridge, chief information officer of Segro, advises organisations to: “Start small with well-scoped pilots that solve real pain points, then bake in repeatable patterns, data contracts, test harnesses, explainability checks and rollback plans, so learning can be scaled without multiplying risk. If you don’t codify how models are approved, deployed, monitored and retired, you won’t get past pilot purgatory.”
Of course, technology alone can’t drive successful AI adoption at scale: the right skills and culture are also essential for embedding AI across the enterprise.
“Multidisciplinary teams that combine technical expertise in AI, security, and governance with deep business knowledge create a foundation for sustainable adoption,” says Professor Haratiannezhadi. “Ongoing training ensures staff acquire advanced AI skills while understanding associated risks and responsibilities.”
Ultimately, an AI operating model is the playbook that enables an enterprise to use AI responsibly and effectively at scale. By drawing together governance, technological infrastructure, cultural change and open collaboration, it supports the shift from isolated experiments to the kind of sustainable AI capability that can drive competitive advantage.
In other words, it’s the foundation for turning ambition into reality, and finally escaping pilot purgatory for good.
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