Web scraping is used for, among other things, getting the vast volumes of publicly available data needed for training algorithms for machine learning (ML). The relationship between data scraping and ML is, however, symbiotic rather than one-sided. On the other side is ML’s ability to improve the fundamental procedures underlying web data gathering, making it […]
When to Use AutoML vs. Manual ML: A Full Guide
Automated machine learning (AutoML) is a set of tools and techniques that automate the design, training, and deployment of machine learning models. AutoML has become essential due to the amount of data involved when creating ML models, helping to save a significant amount of time, human resources, and money. Although manual machine learning is not obsolete, automating […]
What Is a Feature Store in Machine Learning?
A feature store is a centralized platform for managing and serving the features used in machine learning (ML) models. A feature is an individual measurable property or characteristic of data that is used as input to an ML model. In order to build effective ML models, it is critical to have high-quality, well-engineered features that […]
Data Explainability: The Counterpart to Model Explainability
Today, AI and ML are everywhere. Whether it’s everyone playing with ChatGPT (the fastest adopted app in history) or a recent proposal to add a fourth color to traffic lights to make the transition to self-driving cars safer, AI has thoroughly saturated our lives. While AI may seem more accessible than ever, the complexity of AI models has increased exponentially. AI models fall […]
Automated Remediation Can Make Dashboards a Thing of the Past
A convergence of events – including the global pandemic, a shift to remote work, and increased use of the cloud – has accelerated companies’ digital transformations over the past few years and resulted in an ever-growing labyrinth of hybrid networks and endpoints. Digital transformations may have allowed companies to keep up – to a point […]
7 Essential Machine Learning Engineering Skills
Machine learning engineering is a specialized field that combines the principles of computer science, data science, and software engineering with the techniques and methodologies of machine learning. Machine learning engineers are responsible for designing, developing, and implementing machine learning models and systems to solve complex problems or make data-driven predictions and decisions. Machine learning engineering is crucial in various […]
Machine Learning Examples and Applications
A subfield of artificial intelligence, machine learning (ML) uses algorithms to detect patterns in data and solve complex problems. Numerous fields and industries depend on machine learning daily to improve efficiency, accuracy, and decision-making. Recently, ML technologies have been reinforced by two subfields of their own: deep learning (DL) and computer vision. Read on for real-world examples […]
Is Data the Achilles Heel of AI?
As Benjamin Franklin once said, “Nothing is certain except death and taxes.” Add this 21st-century irrefutable fact: The world can’t live without data. With its numbers, characters, facts, and statistics – the operations performed, stored, and analyzed – data has become an irreplaceable facet of daily life. We use data to identify strengths and weaknesses. It helps […]
How Intelligent Enterprise Software Is Transforming Business Processes
Between OpenAI’s research release of its language model ChatGPT and Microsoft’s inclusion of an embedded (and still improving) artificial intelligence (AI) chatbot in its Bing search engine, it’s impossible to escape news about the rise of AI. Of course, these new AI tools are simply the most recent customer-facing applications after decades of development across various computing […]
A Powerful Pair: Modern Data Warehouses and Machine Learning
Artificial intelligence (AI) technologies like machine learning (ML) have changed how we handle and process data. However, AI adoption isn’t simple. Most companies utilize AI only for the tiniest fraction of their data because scaling AI is challenging. Typically, enterprises cannot harness the power of predictive analytics because they don’t have a fully mature data strategy. To […]