By 2028, the global machine learning market will grow to $152.24 billion. As the digital economy continues its massive growth, events like the COVID-19 pandemic further accelerated consumers' demand for digital services. As a result, nearly all businesses across all sectors will seek to harness the power of artificial intelligence (AI) and machine learning. Before businesses can venture deep into the sci-fi movie-inspired possibilities of machine learning, it is important to be aware of its underlying principles and what makes it work. At the heart of every machine learning initiative, data labeling forms a key foundation of this disruptive technology.

Artificial intelligence (AI) creates abundant opportunities for a wide range of intelligent, automated business operations. Two vital capabilities--metadata extraction and data enrichment--rank among the most valuable, commonly used functions for businesses seeking to harness immediate value from organizational data and content. AI-driven techniques for rapidly sorting, filtering, categorizing, and adding context to massive volumes of data can help deliver a distinct business advantage. By combining accessible, cloud-based AI services and customizable, specialized AI tools and training, businesses can shape data and content services to better meet their objectives. Despite the accelerating, never-ending spiral of accumulating content, most businesses aren't gaining the insights they need nor seeing visible operational benefits, as asserted in a Software Development Times article.

One of the world's leading brands in delivering digital experiences, Sitecore brings nearly two decades of expertise in reimagining customer experiences. From its founding in 2001 in Copenhagen, Denmark, the firm has become a powerhouse, known and relied on by its customers for its innovation and ability to push boundaries in technology to create differentiated digital experiences. Given that Sitecore operates in digital mediums, the company routinely handles vast arrays of data and content. Finding ways to maximize the value of that data and content has been an important focus for the company over the years. That is why the advent of artificial intelligence (AI) was of great interest to its leadership, for its potential to treat data and content in entirely new ways.

Helping us all make sense of, and enrich data that is moving along via our data pipes. It is common for our customers to perform sentiment analysis, enrich with tags, and extract names, dates, emails, and other relevant information for streams as they arrive, or as they are being delivered to other destinations. By adding additional tags, meaning, and other metadata, it makes it easier to connect and aggregate data across real-time streams, and transform existing streams into richer topical feeds. We are working on profiling, not just Algorithmia, but a number of other machine learning APIs. As we establish interesting collections of text analysis, deep learning, and other algorithms that can be applied to Streamdata.io streams, we'll publish here on the blog. If you have specific data and content, or machine learning model that you'd like to have delivered as part of your real-time infrastructure let us know. We are happy to prioritize specific types of data or profile more relevant machine learning APIs providers to help expedite your work. We are beginning to ramp up our efforts to profile relevant machine learning models, as the demand from our customers' increases, hoping to satisfy our customers demand for machine learning intelligence as they continue to optimize their streams of data across their organization.

I was reading about how Carbon Black, an endpoint detection and response (EDR) service, was exposing customer data via a 3rd party API service they were using. The endpoint detection and response provider allows customers to optionally scan system and program files using the VirusTotal service. Carbon Black did not realize that premium subscribers of the VirusTotal service get access to the submitted files, allowing a company or government agency with premium access to VirusTotal's application programming interface (API) to mine those files for sensitive data. It provides a pretty scary glimpse at the future of privacy and security in a world of 3rd party APIs if we don't think deeply about the solutions we bake into our applications and services. Each API we bake into our applications should always be scrutinized for privacy and security concerns, making sure end-users aren't being subjected to unnecessary situations.