January 02, 2022

Updating Data Governance: Set Up a Cohesive Plan First

Just because a company has a Data Governance framework it used with a mature technology project, like a data warehouse, does not mean it is sufficient for newer technology initiatives, like machine learning. New business requirements need to be considered, especially where system integration is necessary. For example, Data Quality must be good for all data sets, across the entire enterprise, before machine learning can be applied to a new venture.?Danette McGilvray, President and Principal at Granite Falls Consulting, said, “The cold brutal reality is that the data is not good enough to support machine learning in practically every company.” This is only one of many business needs that crop up before succeeding at such an undertaking. Revisiting Data Governance prior to starting a new data project reduces exposure to mistakenly overlooking prerequisites, and moves toward a unified Data Management approach. Rethinking older Data Governance plans alone does not necessarily lead to a more coherent Data Governance.?

A Hands-On Guide to Outlier Detection with Alibi Detect

Data points that are unusually far apart from the rest of the observations in a dataset are known as outliers. They are primarily caused by data errors (measurement or experimental errors, data collection or processing errors, and so on) or naturally very singular and different behaviour from the norm, for example, in medical applications, very few people have upper blood pressure greater than 200, so If we keep them in the dataset, our statistical analysis, and modelling conclusions will be skewed. To name a few, they can alter the mean and standard deviation values. As a result, it’s critical to accurately detect and handle outliers, either by removing them or reducing them to a predefined value. Outlier detection is thus critical for identifying anomalies whose model predictions we can’t trust and shouldn’t use in production. The type of outlier detector that is appropriate for a given application is determined by the data’s modality and dimensionality, as well as the availability of labelled normal and outlier data and whether the detector is pre-trained (offline) or updated online.

How a startup uses AI to put worker safety first

Computer vision has progressed from an experimental technology to one that can interpret patterns in images and classify them using machine learning algorithms to scale. Advances in deep learning and neural networks enable computer vision uses to increase for enterprises, improving worker safety in the process. Computer vision techniques to reduce worker injuries and improve in-plant safety are based on unsupervised machine learning algorithms that excel at identifying patterns and anomalies in images. Computer vision platforms, including Everguard’s SENTRI360, rely on convolutional neural networks to categorize images and industrial workflows at scale. The quality of the datasets used to train supervised and unsupervised machine learning algorithms determines their accuracy. Convolutional neural networks also require large amounts of data to improve their precision in predicting events, fine-tuned through iterative cycles of machine learning models. Each iteration of a machine learning model then extracts specific attributes of an image and, over time, classifies attributes.?

'Work’ in 2022: What’s next?

Undoubtedly, it needs a complete overhaul of the existing policies, but what we must not forget is that in an evolving environment nothing can be treated as constant. Therefore, swift revision of new policies is very important to match up to the changing scenarios while maintaining people centricity as the central thought. Knowing the employee pulse will be the key to create or revise policies for which regular surveys, town halls, leadership connections are extremely important. Employee safety and well-being will continue to hold the top of mind space and the inclination of workplace culture transformation would be towards empathy and flexibility. Though the challenge of overcoming ‘how-much-is-too-much’ is something that the organizations would need to solve for. They will have to rally together to find the sweet spot to maintain the right balance between productivity and not hampering the work life balance of the employees. ... If data is considered the new oil of the 21st century, ‘Trust’ will become equivalent to it in the post pandemic world making the relationship between the employer and the employee go through a gradual transition where managing expectations from both ends will be essential.

Blockchain technology developments help elevate food safety protocols

Blockchain technologies, something we have been discussing for a few years, are closer than we think. Transparency, traceability, and sustainability are vital to everyone in the industry. The FDA has outlined four core elements in the New Era of Smarter Food Safety Blueprint, and the first of these elements is tech-enabled traceability. Traceability processes are critical to ensure all food items are traced and tracked throughout the supply chain. Traceability is essential for food safety as well as operational efficiency. With a solid traceability program, it is possible to locate a product at any stage of the food chain within the supply chain — literally from farm to table. For this technology to work well, it must be user-friendly and affordable to all — small businesses and large corporations alike. When it is available and widely used, it will minimize foodborne illness outbreaks and assist significantly with speeding up the process of finding the source if an outbreak does occur. Affordable digital technology connecting buyers with validated verified sellers is at the forefront.?

I followed my dreams and got demoted to software developer

I was just a UX person, not a coder. Surrounded by only the most freakishly good developers at Facebook (and then at Stack Overflow), I pushed whatever fantasies I had about coding professionally aside. During these few years in which I’ve been coding in earnest on the side, I also found myself regularly discouraged and confused by the sheer number of possible things that I could learn or do. I can’t count the number of quarter-finished games and barely-started projects I have in my private GitHub repos (actually, I can. It’s 15, and those are just the ones that made it there). Without much formal education in this field, I’d frequently get lost down documentation holes and find myself drowning in the 800 ways of maybe solving the problem that I had. Finally, I came to the conclusion that I needed more structure, and that I wouldn’t be able to get that structure in the hour of useful-brain-time I had after work each day. I started researching bootcamps and doing budget calculations and made plans to leave Stack Overflow.?

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