Machine learning is a subfield of artificial intelligence that involves building algorithms that can automatically learn and improve from data without being explicitly programmed. Machine learning has a wide range of applications in areas such as image and speech recognition, natural language processing, and predictive modeling. In a machine learning project, a model is trained using a labeled dataset, and then the model is used to make predictions or decisions on new, unseen data. There are several steps involved in a typical machine learning project, including initiating the project, identifying business goals, framing the machine learning problem, analyzing the data, designing the model, processing the data, developing the model, deploying the model, testing the model, and deploying to production. Each of these steps is important in ensuring that the model is able to deliver value and achieve the desired outcomes.

Join 30K developers in learning how to responsibly deliver value with ML. "For production ML, I cannot possibly think of a better resource out there ... this resource is the gold standard." "Built some machine learning models? Want to take them to the next level? And I'm using @madewithml to learn how. I am amazed by how much ground it covers starting with data collection, all the way up to k8s model monitoring."

Research predicts the global market for AI in fintech will be worth USD$46.9 billion by 2030 As global business expands, so too does the volume of international cross-border payments: $120 trillion in global B2B payments is processed annually according to research by McKinsey and Visa. And according to Abdul Naushad, President and CEO, Buckzy, Artificial Intelligence (AI) is playing a decisive role in processing these cross-border payments. "Tech advancements and competitive challenges have transformed the payments industry and together have combined to meet both consumer demand and standard banking regulations," said Naushad. A significant part of AI's value in cross-border payments lies in how it substantially improves security. "AI's ability to distinguish patterns and suspicious behaviours is invaluable for identifying fraud and suspicious transactions, and also safely and securely processing sensitive financial documentation," continued Naushad.

If you want to make a positive impact in healthcare with any data science project, keep two things in mind. First, put your customer and end-user at the center of everything. In the end, artificial intelligence (AI) is only as powerful as the human experience it makes possible because solving a problem using AI is about augmenting human expertise, not replacing it. Second, make sure you are addressing a real need. Naturally, these two go hand in hand, and you may find yourself going back and forth between the two.

The operating environment of enterprises is rapidly and fundamentally being altered. Powered by a smarter, more demanding customer spoilt for choices, and doting employees who expect a consumerised experience to deliver value for their organisation, enterprises who tend to delay the adoption to newer and cutting-edge business demands are bound to be left behind and relegated to irrelevance. While some leaders are already moving ahead, many others are still studying the strategic justification for moving beyond BI reporting systems to implement Artificial Intelligence. While the benefits can be profound, the commitment is significant too. In a fast-evolving business environment, strategic objectives need to be paired with the ability to make more frequent, more responsive, and more accurate business decisions.

The promises of artificial intelligence (AI) are countless and only equally matched by the proliferation of AI products and technologies that exist today. This is because AI (and its subsets: machine learning and deep learning) is powerful and here to stay. And it's set to wildly transform how we do business. AI enables a computer program or a machine to think and learn by itself -- without human intervention or command coding. This is the awesome strength of AI and the reason behind all of its hype and explosive market growth projections.

Everyone in business today "feels the need for speed". But probably none more so than application developers, who have found themselves dragged out from behind their cubicle walls and thrust into the spotlight of digital transformation. The most successful developers now work closely with the business side using methodologies like Agile and DevOps, which is also in the name of speed to bring products to light sooner. Yet they must do so with the business goals always in focus. Developers are expected to think about the customer experience, create apps in the cloud, enable them for mobile, AI, IoT, edge -- and now to help secure those apps.

Recently, I came across a Reddit thread on the different roles in data science and machine learning: data scientist, decision scientist, product data scientist, data engineer, machine learning engineer, machine learning tooling engineer, AI architect, etc. It's difficult to be effective when the data science process (problem framing, data engineering, ML, deployment/maintenance) is split across different people. It leads to coordination overhead, diffusion of responsibility, and lack of a big picture view. IMHO, I believe data scientists can be more effective by being end-to-end. Here, I'll discuss the benefits and counter-arguments, how to become end-to-end, and the experiences of Stitch Fix and Netflix. I find these definitions to be more prescriptive than I prefer. Instead, I have a simple (and pragmatic) definition: An end-to-end data scientist can identify and solve problems with data to deliver value.

Artificial intelligence (AI) has become a crucial part of our day-to-day. Self-learning machines are embedded in services or devices used by three-quarters of global consumers. And algorithms choose what news we read and the entertainment we consume. So let's be clear – to some extent, the machines have already taken over and with little resistance. I recently had the opportunity to be part of a campaign involving a series of focus groups to better understand the thoughts and feelings of various experts towards AI.

THE BIGGEST CHANGE being brought about by AI is not human replicas, but the emergence of digital operating models. These models aren't the sexy, headline-grabbing side of AI, but they are profoundly affecting how we do business and the way leaders of the twenty-first century must think. Marco Iansiti and Karim Lakhani, professors at Harvard Business School, explore these changes in Competing in the Age of AI with examples of businesses in many industries. "When a business is driven by AI, software instructions, and algorithms make up the critical path in the way the firm delivers value." And thus, how we think about the work we do and how we compete in the marketplace. The business plan describes the problem that is being solved for the customer--a reason to buy.