Artificial intelligence and machine learning are every forward-thinking entrepreneur’s favorite buzzwords. However, it seems that the world’s largest tech companies are the only ones effectively implementing AI and ML (at least to a financially beneficial scale). The exclusivity of machine learning projects contradicts the hype surrounding them.
After four years of working with machine learning technology, our founders noticed a pattern in the issues that the implementation, scaling, and continuous monitoring of ML projects. No matter the intended outcome, the problems we ran into creating these systems for our clients were the same.
Less than 0.5% of the more than 2.5 quintillion bytes of data generated daily are ever analyzed and only a fraction of that number is put to use effectively by businesses. This data, coupled with the emergence of Artificial intelligence and Machine Learning offers an immense promise of opportunity for businesses moving forward. However, implementing machine learning is a significant challenge for every modern organization. While well-equipped data science teams can piece together a variety of libraries and technologies to train models; unifying and scaling these workflows across individuals and teams is highly troublesome.
While integrating ML projects into a variety of businesses, the two most persistent problems our founders ran into was the need to use different tools for each part of the process and the disconnect between coworkers assigned to the project including data science teams, analytics teams, project managers, and computer scientists. Machine Learning in many ways is a brave new world for businesses. Adoption can be cumbersome and risky — lacking the necessary transparency and unified platform to accurately drive results and revenue.
Skyl was born as a response to these challenges. Delivering an end-to-end Machine Learning platform that bridges the divide between what businesses need and how they get there while reducing risk and planning for growth. The Skyl platform accelerates ML adoption with a transparent, standards-based environment for getting up-and-running fast and managing all aspects of labeling, testing, model training, and delivering results. We ensure that no aspect of the machine learning process needs to be outsourced, and each team can communicate their grievances with ease.
At Skyl, our vision is to empower every organization on the planet to answer impossible questions with data and artificial intelligence; enabling people and enterprises to use Machine Learning to solve problems that are shaping their business and impacting our world.
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Jump-Start AI Adoption and Lead the Pack
You only need to look towards the world’s most fast-growing businesses today to understand the implications of ML on the future of business. Unicorns such as Uber and Airbnb are using ML in everything from how internal operations such as support ticket responses are managed to how their apps offer exceptional user experiences. Uber’s Michelangelo, and Airbnb’s BigHead projects in many ways have begun to shed light into how future business leaders will use data and algorithms in a more sophisticated, mainstream way. However these home-spun projects also bring into focus the challenges involved for any business building and effectively managing, monitoring, and testing ML projects.
The results of these projects are evident in how these businesses have managed to drive massive market adoption while revolutionizing the transportation and lodging industries respectively. It’s easy to overlook the amount of time, uncertainty, and multi-million dollar investments required to actually bring these projects to fruition. For example, Uber’s Michaelangelo is a project that started in 2015, and could only be implemented in 2017.
Unfortunately, not every business has the resources to create their own platform through a dedicated team and years of hard work. Skyl.ai aims to make the sophisticated machine learning automation of tech giants attainable for any business.
Bringing Skyl Into Your Business
In order to make ML automation accessible, Skyl.ai walks the user through every step of the machine learning process from data collection to model monitoring. Skyl.ai modules (as pictured below) can be used for various machine learning problems including Computer Vision, Natural Language Processing such as text classification, extraction, summarization, and several kinds of predictions.
Skyl.ai is a unified and collaborative platform for streamlining the end-to-end machine learning workflow. The platform facilitates data collection, preparation, training, and monitoring of machine learning models, allowing data science teams to proactively prevent errors, correct data bias, and create visibility at all stages of the machine learning life-cycle. More quickly than ever before, businesses can adopt machine learning to derive outcomes and solve problems through the use of Skyl.ai.