Building the next generation of enterprise machine learning and artificial intelligence systems
Enterprise Artificial Intelligence is starting to affect every industry we know. Algorithms that are becoming deeply embedded in the back-end; analyzing data, finding patterns, creating powerful knowledge graphs that power search and recommendations, providing real-time intelligence and much much more.
Building intelligent systems are still hard and require large sets of training data and complex algorithms, costing enterprises millions of dollars and taking years to build and train.
Times are changing, more and more enterprises artificial intelligence startups are figuring out how to build intelligent systems with smaller amounts of data and much simpler algorithms; enabling organizations of all sizes to take full advantage of AI, while simultaneously getting high returns on their investments.
Logyc is one of the startups that figured out how to build incredibly smart systems in a fraction of the cost and time, without needing large amounts of training data.
In this weekly series, we will take a look at what makes Logyc so powerful, and the lessons learned that could make your intelligent system much more efficient and valuable.
ThinkLogyc series will cover ten topics that one has to consider when building machine learning solutions which include:
The system needs to be transparent and simple. Adding one algorithm at a time until the desired business outcome is achieved is the key to scalability and high-performance.
Outdated data costs U.S. alone over 3 Trillion dollars. Adding IoT & APIs to your system can help keep the data fresh while adding human-augmented machine learning layer can help your system adapt to ever-changing, dynamic world.
Building AI system that considers millions of variables simultaneously is critical in the dynamically changing world that has millions of internal and external factors affecting business outcomes.
The truth is, no enterprise has all the labeled data they need to build truly intelligent systems. Fortunately acquiring external data is now easier than ever.
Data scientists often lack domain expertise required to go beyond the statistical relationships and patterns to identify key insights. Leverage the domain knowledge available within the enterprise can significantly improve the performance of the system; as well as, the business outcomes.
One-fits-all solutions rarely benefit more than one group of people. Truly intelligent systems should be flexible enough for the end-users to be able to modify the inputs and rules, to tailor the system to meet their business needs.
Global enterprises require global solutions, while local employees of those enterprises require localized solutions. From country to country, from city to city, not only there can be different languages, but the business rules, laws and compliance can be completely different. Are you building a system that can easily adapt to those differences?
Think Artificial Intelligence
Over the last decade, the amount of data has increased drastically, and computer processing capabilities have grown so much that AI is now becoming a truly powerful tool. The hype surrounding AI is just as profound, using the latest techniques such as neural networks can be very useful, but especially enterprises solutions should focus on the solutions architecture first, data second, and AI third. Starting with a question, how can data science transform your business and your industry should be more prominent than how can we use data science to find patterns.
Over the next ten weeks, we are looking forward to exploring these topics with you and if you want to be the first to receive those articles in your email inbox, then subscribe to our mailing list by clicking here.