Deploying Machine Learning into production: Don’t do Labs.

In my experience building analytics products at Best Buy Canada, applied data science projects rarely fail because of the science. They fail because the model couldn’t be integrated into existing systems and business operations. We have had sound models showing good accuracy, even with proofs of concept demonstrating value, yet still fail to get deployed into production. The challenge isn’t POCs, its scaling.

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How to retain your institutional knowledge when employees retire (& how can AI simplify this)

Since skills, knowledge and experience are vital to a successful business and the pace in which it innovates, retaining existing institutional knowledge is an increasing priority. How can you guarantee that your company’s know-how won’t just walk out the door and jeopardise your brand and positioning? The short answer is: You can’t. But there are ways that utilising a combination of analytics, IoT, and AI techniques, along with corporate training and knowledge replacement strategies, can help.

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How the Dunning-Kruger effect can explain why your data science proposals don’t get buy-in

Consider the Dunning-Kruger effect to get your proposals taken seriously.
Many brilliant data science proposals never make it beyond the paper they’re written on. I’d like to start off by painting you a picture. Imagine you’re an experienced data scientist. You work for a small company and report into a team of directors who lead the company and are responsible for all the decisions made. Only proposals that get their buy-in can be implemented.

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The future of commercial Deep Learning

How do we balance its benefits and integrity going forward? Underlying modern AI is deep learning, algorithms through which computers learn to perform intelligent tasks without being explicitly programmed. These algorithms train artificial neural networks, which iteratively learn relationships between inputs and outputs through copious examples. My solution to preserving the benefits of commercial deep learning while prioritizing its integrity is three-fold:

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Modelling Risk: the absolute and the relative

What is the risk of a new creditor to default on their loan? what is the “risk” of watching a certain movie on Netflix given a certain viewing history? What is my risk of dying given a certain diagnosis and how is this affected by a certain treatment? Risks are all around us, and quantifying these risks is becoming increasingly popular. Providing the right kind of analysis to these key questions is crucial to making the right decisions.

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How Artificial Intelligence will drive Predictive Analysis to the next level

Scientists pioneer in innovative ways of creating revolutionary healthcare insights through artificial intelligence prediction. Based on a patient’s eye scan, their system can make predictions against the patient’s risk of experiencing a severe cardiac incident. It achieves by training a Machine Learning system with medical data, including the age, blood pressure, and smoking habits of three hundred thousand patients.

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Building AI Leadership Brain Trust for Board Directors and CEOs – Blog Series: Emotional and Social Intelligence Skills

This blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEO’s to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results. This blog drills down to explain ten Emotional and Social Intelligence skills required in building an AI Leadership Brain Trust.

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AI jobs in 2021: here are some key trends

There’s no doubt about it – Artificial Intelligence has been a bit of a buzzword this year. Artificial intelligence has been established as the main driver of emerging technologies such as big data, robotics, and the IoT. So, what do the next 12 months look like for AI?
As a result of the global pandemic, consumer trends have changed significantly, which has resulted in some notable trends in the world of AI for 2021…

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Machine Learning: Automated DevOps and threat identification

Machine Learning (ML) and Artificial Intelligence (AI) is a technology that is still finding its footing in the commercial sector. Although few systems are touted as a complete solution, there are many new AI/ML based companies that are capitalising on the benefits, and traditional business will need to follow suit. ML Ops, the ML equivalent of DevOps will become increasingly important.

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Data quality from First Principles

The right way to think about Data Quality, from Kimball and Uber’s points of view. If you’ve spent any amount of time in business intelligence, you would know that data quality is a perennial challenge. It never really goes away. For instance, how many times have you been in a meeting, and find that someone has to vouch for the numbers being presented?

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How machines are changing the way companies talk

Anyone who’s ever been on an earnings call knows company executives already tend to look at the world through rose-colored glasses, but a new study by economics and machine learning researchers says that’s getting worse, thanks to Machine Learning.

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Why should you, the AI Product Manager, care about baseline models

Baseline models can help you get started with very little effort. You can leverage them to ensure that your team is on the right track. You are a Product Manager and want to incorporate Machine Learning or Deep Learning (a sub-field of Machine Learning) into your Product. You have heard that incorporating these algorithms into your Product can lead to several benefits but are wondering where to get started?

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Few AI startups release revenue numbers: Will any of them ever make a profit?

Only four Western and two Chinese AI companies report income, and all have big losses. CrowdStrike and c3.ai both did IPOs and had losses equal to 30% and 40% of revenues respectively in 2019, and 13% and 40% respectively in 2020. Nest’s losses were 85% of revenues in 2017[1] and DeepMind’s losses were four and 1.7 times its revenues in 2018[2] and 2019 respectively[3] causing Google to write off $1.3 Billion in debts.

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