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We've been seeing the headlines for years: "Researchers find flaws in the algorithms used…" for nearly every use case for AI, including finance, health care, education, policing, or object identification. Most conclude that if the algorithm had only used the right data, was well vetted, or was trained to minimize drift over time, then the bias never would have happened. There are several practical strategies that you can adopt to instrument, monitor, and mitigate bias through a disparate impact measure. For models that are used in production today, you can start by instrumenting and baselining the impact live. For analysis or models used in one-time or periodic decision making, you'll benefit from all strategies except for live impact monitoring.

The space projects have been dominated by government bodies until we saw the ambitious companies such as SpaceX and Blue Origin diving into this diverse area. These two are the most prominent names in the private space community and are often put on a face-off due to the similarity of its founders in other areas as well. Owned by two of the most powerful businessmen of all time -- Elon Musk and Jeff Bezos, they have been on the competition radar for their interest in the area of autonomous vehicles. Similarly, in the space segment, while the two companies might look quite similar in its attempts to explore space, the ideology and the approach of these companies vary quite significantly. But one thing cannot be denied that they both are developing large, reusable vehicles capable of carrying people and satellites across space. While we have often heard about SpaceX's missions and launches over the past few years, Blue Origin does not come out to be so ambitious in gaining traction.

-- Predicting s ales opportunities outcome is a core to successful business management and revenue forecasting . Conventionally, this prediction has relied mostly on subjective human evaluations in the process of business to business (B2B) sales decision making. Here, we proposed a practical Machine Learning (ML) workflow to empower B2B sales outcome (win/lose) pre diction within a cloud - based computing platform: Microsoft Azure Machine Learning Service (Azure ML). This workflow consists of two pipelines: 1) a n ML pipeline that trains probabilistic predictive models in parallel on the closed sales opportunities data enhanced with an extensive feature engineering procedure for automated selection and parameterization of an optimal ML model and 2) a Prediction pipeline that uses the optimal ML model to estimate the likelihood of win n ing new sales opportunities as well a s predicting their outcome using optimized decision boundaries. The p erformance of the proposed workflow was evaluated on a real sales dataset of a B2B consulting firm. In the Business to Business (B2B) commerce, companies compete to win high - valued sales opportunities to maximize their profitability. In this regard, a key factor for maintain ing a successful B2B business is the task of determining the outcome of sales opportunities.