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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Agenda Digitale: The Five Pillars of the Cloud According to NIST – A Compass for Businesses and Public Administrations
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Jun 26, 2025 7 min read

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By Lokesh Vij, Professor of Cloud Computing Infrastructure, Cloud Development, Cloud Computing Automation and Ops and Cloud Data Stacks at OPIT – Open Institute of Technology

NIST identifies five key characteristics of cloud computing: on-demand self-service, network access, resource pooling, elasticity, and metered service. These pillars explain the success of the global cloud market of 912 billion in 2025

In less than twenty years, the cloud has gone from a curiosity to an indispensable infrastructure. According to Precedence Research, the global market will reach 912 billion dollars in 2025 and will exceed 5.1 trillion in 2034. In Europe, the expected spending for 2025 will be almost 202 billion dollars. At the base of this success are five characteristics, identified by the NIST (National Institute of Standards and Technology): on-demand self-service, network access, shared resource pool, elasticity and measured service.

Understanding them means understanding why the cloud is the engine of digital transformation.

On-demand self-service: instant provisioning

The journey through the five pillars starts with the ability to put IT in the hands of users.

Without instant provisioning, the other benefits of the cloud remain potential. Users can turn resources on and off with a click or via API, without tickets or waiting. Provisioning a VM, database, or Kubernetes cluster takes seconds, not weeks, reducing time to market and encouraging continuous experimentation. A DevOps team that releases microservices multiple times a day or a fintech that tests dozens of credit-scoring models in parallel benefit from this immediacy. In OPIT labs, students create complete Kubernetes environments in two minutes, run load tests, and tear them down as soon as they’re done, paying only for the actual minutes.

Similarly, a biomedical research group can temporarily allocate hundreds of GPUs to train a deep-learning model and release them immediately afterwards, without tying up capital in hardware that will age rapidly. This flexibility allows the user to adapt resources to their needs in real time. There are no hard and fast constraints: you can activate a single machine and deactivate it when it is no longer needed, or start dozens of extra instances for a limited time and then release them. You only pay for what you actually use, without waste.

Wide network access: applications that follow the user everywhere

Once access to resources is made instantaneous, it is necessary to ensure that these resources are accessible from any location and device, maintaining a uniform user experience. The cloud lives on the network and guarantees ubiquity and independence from the device.

A web app based on HTTP/S can be used from a laptop, tablet or smartphone, without the user knowing where the containers are running. Geographic transparency allows for multi-channel strategies: you start a purchase on your phone and complete it on your desktop without interruptions. For the PA, this means providing digital identities everywhere, for the private sector, offering 24/7 customer service.

Broad access moves security from the physical perimeter to the digital identity and introduces zero-trust architecture, where every request is authenticated and authorized regardless of the user’s location.

All you need is a network connection to use the resources: from the office, from home or on the move, from computers and mobile devices. Access is independent of the platform used and occurs via standard web protocols and interfaces, ensuring interoperability.

Shared Resource Pools: The Economy of Scale of Multi-Tenancy

Ubiquitous access would be prohibitive without a sustainable economic model. This is where infrastructure sharing comes in.

The cloud provider’s infrastructure aggregates and shares computational resources among multiple users according to a multi-tenant model. The economies of scale of hyperscale data centers reduce costs and emissions, putting cutting-edge technologies within the reach of startups and SMBs.

Pooling centralizes patching, security, and capacity planning, freeing IT teams from repetitive tasks and reducing the company’s carbon footprint. Providers reinvest energy savings in next-generation hardware and immersion cooling research programs, amplifying the collective benefit.

Rapid Elasticity: Scaling at the Speed ​​of Business

Sharing resources is only effective if their allocation follows business demand in real time. With elasticity, the infrastructure expands or reduces resources in minutes following the load. The system behaves like a rubber band: if more power or more instances are needed to deal with a traffic spike, it automatically scales in real time; when demand drops, the additional resources are deactivated just as quickly.

This flexibility seems to offer unlimited resources. In practice, a company no longer has to buy excess servers to cover peaks in demand (which would remain unused during periods of low activity), but can obtain additional capacity from the cloud only when needed. The economic advantage is considerable: large initial investments are avoided and only the capacity actually used during peak periods is paid for.

In the OPIT cloud automation lab, students simulate a streaming platform that creates new Kubernetes pods as viewers increase and deletes them when the audience drops: a concrete example of balancing user experience and cost control. The effect is twofold: the user does not suffer slowdowns and the company avoids tying up capital in underutilized servers.

Metered Service: Transparency and Cost Governance

The dynamic scale generated by elasticity requires precise visibility into consumption and expenses : without measurement there is no governance. Metering makes every second of CPU, every gigabyte and every API call visible. Every consumption parameter is tracked and made available in transparent reports.

This data enables pay-per-use pricing , i.e. charges proportional to actual usage. For the customer, this translates into variable costs: you only pay for the resources actually consumed. Transparency helps you plan your budget: thanks to real-time data, it is easier to optimize expenses, for example by turning off unused resources. This eliminates unnecessary fixed costs, encouraging efficient use of resources.

The systemic value of the five pillars

When the five pillars work together, the effect is multiplier . Self-service and elasticity enable rapid response to workload changes, increasing or decreasing resources in real time, and fuel continuous experimentation; ubiquitous access and pooling provide global scalability; measurement ensures economic and environmental sustainability.

It is no surprise that the Italian market will grow from $12.4 billion in 2025 to $31.7 billion in 2030 with a CAGR of 20.6%. Manufacturers and retailers are migrating mission-critical loads to cloud-native platforms , gaining real-time data insights and reducing time to value .

From the laboratory to the business strategy

From theory to practice: the NIST pillars become a compass for the digital transformation of companies and Public Administration. In the classroom, we start with concrete exercises – such as the stress test of a video platform – to demonstrate the real impact of the five pillars on performance, costs and environmental KPIs.

The same approach can guide CIOs and innovators: if processes, governance and culture embody self-service, ubiquity, pooling, elasticity and measurement, the organization is ready to capture the full value of the cloud. Otherwise, it is necessary to recalibrate the strategy by investing in training, pilot projects and partnerships with providers. The NIST pillars thus confirm themselves not only as a classification model, but as the toolbox with which to build data-driven and sustainable enterprises.

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ChatGPT Action Figures & Responsible Artificial Intelligence
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Jun 23, 2025 6 min read

You’ve probably seen two of the most recent popular social media trends. The first is creating and posting your personalized action figure version of yourself, complete with personalized accessories, from a yoga mat to your favorite musical instrument. There is also the Studio Ghibli trend, which creates an image of you in the style of a character from one of the animation studio’s popular films.

Both of these are possible thanks to OpenAI’s GPT-4o-powered image generator. But what are you risking when you upload a picture to generate this kind of content? More than you might imagine, according to Tom Vazdar, chair of cybersecurity at the Open Institute of Technology (OPIT), in a recent interview with Wired. Let’s take a closer look at the risks and how this issue ties into the issue of responsible artificial intelligence.

Uploading Your Image

To get a personalized image of yourself back from ChatGPT, you need to upload an actual photo, or potentially multiple images, and tell ChatGPT what you want. But in addition to using your image to generate content for you, OpenAI could also be using your willingly submitted image to help train its AI model. Vazdar, who is also CEO and AI & Cybersecurity Strategist at Riskoria and a board member for the Croatian AI Association, says that this kind of content is “a gold mine for training generative models,” but you have limited power over how that image is integrated into their training strategy.

Plus, you are uploading much more than just an image of yourself. Vazdar reminds us that we are handing over “an entire bundle of metadata.” This includes the EXIF data attached to the image, such as exactly when and where the photo was taken. And your photo may have more content in it than you imagine, with the background – including people, landmarks, and objects – also able to be tied to that time and place.

In addition to this, OpenAI also collects data about the device that you are using to engage with the platform, and, according to Vazdar, “There’s also behavioral data, such as what you typed, what kind of image you asked for, how you interacted with the interface and the frequency of those actions.”

After all that, OpenAI knows a lot about you, and soon, so could their AI model, because it is studying you.

How OpenAI Uses Your Data

OpenAI claims that they did not orchestrate these social media trends simply to get training data for their AI, and that’s almost certainly true. But they also aren’t denying that access to that freely uploaded data is a bonus. As Vazdar points out, “This trend, whether by design or a convenient opportunity, is providing the company with massive volumes of fresh, high-quality facial data from diverse age groups, ethnicities, and geographies.”

OpenAI isn’t the only company using your data to train its AI. Meta recently updated its privacy policy to allow the company to use your personal information on Meta-related services, such as Facebook, Instagram, and WhatsApp, to train its AI. While it is possible to opt-out, Meta isn’t advertising that fact or making it easy, which means that most users are sharing their data by default.

You can also control what happens with your data when using ChatGPT. Again, while not well publicized, you can use ChatGPT’s self-service tools to access, export, and delete your personal information, and opt out of having your content used to improve OpenAI’s model. Nevertheless, even if you choose these options, it is still worth it to strip data like location and time from images before uploading them and to consider the privacy of any images, including people and objects in the background, before sharing.

Are Data Protection Laws Keeping Up?

OpenAI and Meta need to provide these kinds of opt-outs due to data protection laws, such as GDPR in the EU and the UK. GDPR gives you the right to access or delete your data, and the use of biometric data requires your explicit consent. However, your photo only becomes biometric data when it is processed using a specific technical measure that allows for the unique identification of an individual.

But just because ChatGPT is not using this technology, doesn’t mean that ChatGPT can’t learn a lot about you from your images.

AI and Ethics Concerns

But you might wonder, “Isn’t it a good thing that AI is being trained using a diverse range of photos?” After all, there have been widespread reports in the past of AI struggling to recognize black faces because they have been trained mostly on white faces. Similarly, there have been reports of bias within AI due to the information it receives. Doesn’t sharing from a wide range of users help combat that? Yes, but there is so much more that could be done with that data without your knowledge or consent.

One of the biggest risks is that the data can be manipulated for marketing purposes, not just to get you to buy products, but also potentially to manipulate behavior. Take, for instance, the Cambridge Analytica scandal, which saw AI used to manipulate voters and the proliferation of deepfakes sharing false news.

Vazdar believes that AI should be used to promote human freedom and autonomy, not threaten it. It should be something that benefits humanity in the broadest possible sense, and not just those with the power to develop and profit from AI.

Responsible Artificial Intelligence

OPIT’s Master’s in Responsible AI combines technical expertise with a focus on the ethical implications of AI, diving into questions such as this one. Focusing on real-world applications, the course considers sustainable AI, environmental impact, ethical considerations, and social responsibility.

Completed over three or four 13-week terms, it starts with a foundation in technical artificial intelligence and then moves on to advanced AI applications. Students finish with a Capstone project, which sees them apply what they have learned to real-world problems.

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