data scientist


Adventures With Artificial Intelligence and Machine Learning

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Since October of last year I have had the opportunity to work with an startup working on automated machine learning and I thought that I would share some thoughts on the experience and the details of what one might want to consider around the start of a journey with a "data scientist in a box". I'll start by saying that machine learning and'artificial intelligence has almost forced itself into my work several times in the past eighteen months, all in slightly different ways. The first brush was back in June 2018 when one of the developers I was working with wanted to demonstrate to me a scoring model for loan applications based on the analysis of some other transactional data that indicated loans that had been previously granted. The model had no explanation and no details other than the fact that it allowed you to stitch together a transactional dataset which it assessed using a naïve Bayes algorithm. We had a run at showing this to a wider audience but the palate for examination seemed low and I suspect that in the end the real reason was we didn't have real data and only had a conceptual problem to be solved.


Adventures With Artificial Intelligence and Machine Learning

#artificialintelligence

Since October of last year I have had the opportunity to work with an startup working on automated machine learning and I thought that I would share some thoughts on the experience and the details of what one might want to consider around the start of a journey with a "data scientist in a box". I'll start by saying that machine learning and'artificial intelligence has almost forced itself into my work several times in the past eighteen months, all in slightly different ways. The first brush was back in June 2018 when one of the developers I was working with wanted to demonstrate to me a scoring model for loan applications based on the analysis of some other transactional data that indicated loans that had been previously granted. The model had no explanation and no details other than the fact that it allowed you to stitch together a transactional dataset which it assessed using a naïve Bayes algorithm. We had a run at showing this to a wider audience but the palate for examination seemed low and I suspect that in the end the real reason was we didn't have real data and only had a conceptual problem to be solved.


Opinion: Why a Data Scientist is the hottest job in tech right now Venture

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Written for Daily Hive by Steve Astorino, vice president of Development, Cognos, and Planning Analytics at Hybrid Data Management and Director of IBM Canada Labs, the largest software development organization in Canada. He is the co-author of "Artificial Intelligence: Evolution and Revolution" Harvard Business Review once called Data Scientists "the sexiest job of the 21st century." So what exactly is a data scientist, and what makes it such a hot job in today's market? Despite its rise across Canadian and global business sectors, data science is still largely unknown or misunderstood by the public at large. In one sentence, a data scientist understands how to collect, use, and analyze data using a machine learning model to solve real-world problems.


MLOps--the path to building a competitive edge

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Enterprises today are transforming their businesses using Machine Learning (ML) to develop a lasting competitive advantage. From healthcare to transportation, supply chain to risk management, machine learning is becoming pervasive across industries, disrupting markets and reshaping business models. Organizations need the technology and tools required to build and deploy successful Machine Learning models and operate in an agile way. MLOps is the key to making machine learning projects successful at scale. It is the practice of collaboration between data science and IT teams designed to accelerate the entire machine lifecycle across model development, deployment, monitoring, and more.


I am an AI Engineer

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I am an AI Engineer. Now that I've said it though, what exactly does that mean? Well, a few months ago I myself didn't know what it meant to be an AI engineer. Candidly, I wasn't really clear on what the role was or the problem it was solving. For years we'd gone out on meetings and talked with the typical "target client" for data science about their projects and the impact of infrastructure on their delivery.


10 Latest Data Science Job Openings In Bengaluru

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Data science jobs are one of the highest paying jobs of this decade. The democratization of analytics tools along with the rise in reading resources has drawn more attention towards this thriving sector. In India, data science jobs are on a rise as every company from startup to industry leaders are incorporating algorithmic solutions into their workflows. In this article, we bring you top 10 data science jobs in Bengaluru -- the Silicon Valley of India. This job is for those who like to write smart algorithms and deal with complex problems that require a mix of AI/ML, big data, computer vision, NLP and a dash of probability and statistics to solve.


The costs of random acts of AI: The right culture is the new strategy imperative

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In my experience leading transformation initiatives, I have seen many similarities between broader business transformations and AI transformations, but there is one key difference – in AI, the world is still highly experimental. In fact, 3 out of 4 of AI projects fail to show positive returns on investment. Because of this, there hasn't been a map of what AI success at scale really looks like. It remains uncharted territory for enterprises. In October 2019, we commissioned a study with Inc.digital, where we talked to 550 executives across a variety of industries to understand why enterprises don't more frequently see tangible, measurable results from AI. Through this research, we were able to determine the DNA of the few that are successful with their AI strategy – the one common thread is keeping the AI strategy, data, technology, people and processes close to the core and controlled.


Just How Much Does the Future Depend on AI?

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Summary: Looking at the 12 hottest world-changing segments in the VC-funded world shows that AI will play a key role. From the inside of the data science profession looking out it's easy to imagine that almost everything that is or will be important somehow depends on AI. Maybe that's true, but how do we tell? First of all we'd have to make a list of all the tech trends that are destined to be game changers over the mid-term, say the next 5 to 15 years. Then we could examine each one for AI content and get a better idea about just how important AI is.


Where will data science and audience insights take us in 2020?

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This will make this area of data science even more commonplace not only among top tech companies, but also small and medium-sized businesses across various verticals. However, one aspect which is potentially underrated when looking at the big trends, in terms of the future of data science, is around language frameworks used to make the everyday data science tasks possible. Today, there are two major frameworks, R or Python (or in more pragmatic data science circles, both!). One is praised for having the most beautifully designed data wrangling syntax and plotting libraries, the other for its expressiveness and having the best deep learning libraries available today. However, both suffer from being relatively slow as they're higher level languages.


It's Time to Act on the AI Talent Shortage

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There's been a lot of talk about the shortage of data scientists and engineers, and unfortunately, the problem is going to get worse before it gets better. When you consider the increasing demand for Artificial Intelligence (AI) expertise in all types of businesses and the role that AI is playing in making companies more competitive, there's no question that it's a serious issue. We're seeing AI applications across industries, in situations as diverse as saving the environment, predicting who will be re-admitted to hospitals or which medical device might fail, and it seems like use cases keep on coming. As Andrew Ng, a noted computer scientist, was quoted as saying, "I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years." And, industry statistics bear that out.