Data science is an interdisciplinary field, which combines statistical knowledge, substantive expertise in many fields of computer science, processes, algorithms, and systems to extract value from data. It is an enabler of enhanced innovation and increased productivity. It allows us to reveal trends and gain insights to solve business challenges, often uncovering new and transformative opportunities along the way
Machine Learning, at its most basic form is where a computer system is fed large amounts of data, which it then uses to learn how to carry out a specific task. The algorithms adaptively improve their performance as the number of samples available for learning increases
Deep Learning is basically Machine Learning on steroids. Key to the process of deep learning is neural networks. Neural Networks are brain-inspired networks of interconnected layers of algorithms, called neurons, that feed data into each other, and which can be trained to carry out specific tasks by modifying the importance attributed to input data as it passes between the layers. There are multiple layers to process features, and generally, each layer extracts some piece of valuable information. During training of these neural networks, the weights attached to different inputs will continue to be varied until the output from the neural network is very close to what is desired, at which point the network will have ‘learned’ how to carry out a particular task
Most Machine Learning Teams today don’t have systems in place to build reliable, uniform, and reproducible pipelines for managing their Machine Learning Projects. Machine learning in practice involves much more than using the latest technologies and modeling frameworks.
At RandomTrees, we provide standardized workflows, Tools and Best Practices to accelerate the application of ML though an end-to-end system that enables users across the company to seamlessly build, deploy, and operate machine learning solutions at scale using industry-standard open-source technologies and cloud-native containerized microservices AI architecture
The process of analyzing and acting upon data is iterative, but this is how our work typically flows for an ML project