Engineering at Oak-Tree

Resources and Reference

Strategy Guides

• Figure Eight: How to put active learning to work for your enterprise. What is involved in choosing the right machine learning strategy, locating data, labeling, and assessing model accuracy?
• Figure Eight: The State of AI and Machine Learning (2019). The number of organizations using artificial intelligence and machine larning has skyrocketed. Today, more than one-third of enterprise organizations use AI in some capacity, and AI deployments have grown by 270% during the last four years. As companies figure out how to make AI and Data Science initiatives successful, two groups have emerged. Technical practitioners, who are responsible for writing code and creating the systems that enable the futuristic capabilities; and stakeholders (managers, directors, and executives) tasked with overseeing AI initiatives. To really enjoy the benefits of AI, it is necessary to bridge the gaps between the two and embrace the commonality between efforts to adopt AI. This guide explores some of the challenges in adopting AI and data driven decision making for an organization, and provides a look at how some groups have successfully integrated AI into their broader initiatives.

Data Preparation

• Figure Eight: The Essential Guide to Training Data. It's common knowledge that every machine learning solution needs a good algorithm powering it. Plenty of ink is spilled on tech sites about advances in deep learning and how the newest models are driving business success for everything from personalized shopping to national security. But to make machine learning and AI work, you need data. Data is the oil, the model is the car. Data is the ingredients, the algorithm is the recipe. Neither works without the other. In this guide, machine learning agency Figure 8 covers many of the finer points needed about finding and enriching training data to ensure that machine learning projects are successful.