Disclosure: I’m not really a data scientist these days, I’m a founder of a tech startup (which has a core AI component). These are my unfair, biased, and prejudiced views based on experience of being a professional data scientist for something like a decade. So firstly, I think it’s worth declaring that machine learning and… Continue reading How can Data Scientists survive layoffs?
What is synthetic media? Synthetic Media includes artificially-generated video, voice, images or text, where AI takes on part (or all) of the creative process. This falls under the broader landscape of synthetic, artificial or virtual reality (photo-realistic AR/VR). One of the more powerful things about modern machine learning or AI methods is that they can… Continue reading Synthetic Media – What’s coming next?
Unbundling of people or ‘rise of the creator class’ If you work in Tech you end up exposed to trends, and if you’re a product-focused engineer you think a lot about ‘product’, product strategy and what’s ‘coming next’. One question in our current ecosystem is ‘what’s next after marketplaces’ we’ve already seen very successful companies… Continue reading The rise of the creator class
One of the distinctions I like to make about modern ‘Data Science’ is between ‘data science for decision support’ and ‘machine learning’. h Basically speaking machine learning, which is often product-focused – is generally something like ‘there’s this problem in fraud, credit scoring’ and we need an automated and deployed system. You’ll often work super… Continue reading Data Science for Decision Support: Or why Bayesian Analysis matters
I’ve been working on software products for several years now. And while a lot of my work has been greenfield, I’ve struggled to articulate the different kinds of work that happens in product development. Des Traynor one of the founders of Intercom had a great talk about this. (Source: Video) Often people don’t understand the two… Continue reading The two kinds of work in Software Products
I recently caught up with Noelle Saldana and she shared some of her experiences as a Data Scientist and Product Management expert. She’s got a strong technical background and has worked for Pivotal a leading technology consultancy helping Forbes 500 learn how to leverage Machine Learning. She’s recently joined Salesforce where she’ll be wearing a… Continue reading “Much of our day-to-day lives are touched and improved by Data”
I’ve got an online course called Probabilistic Programming Primer You may be wondering what that’s all about, so I put together a video explainer all about it. Building easy to interpret models isn’t a nice to have anymore it is the reason people pay for models in the first place. And as we see more… Continue reading Introduction to Probabilistic Programming Primer
I recently put together a survey of over 100 data scientists and analysts. There’ll be a report coming super soon, but before then I wanted to share the infographic. We’ll go into more detail in the report. If you’d like to get access to the report you should sign up here 70 percent of data scientists… Continue reading State of PPL: How are Bayesian methods used in industry?
I’ve been contributing on and off to PyMC3 and other projects for a few years now. I’m still learning a lot about Bayesian Statistics and building software. I intend to continue to work on this stuff. Chris Fonnesbeck – recently did a talk at NeurIPS where he talks about some of the stuff we’ve learned in… Continue reading 3 key lessons from being an OSS developer
Probabilistic Programming versus Machine Learning In the past ten years, we’ve seen an explosion in Machine Learning applications, these applications have been particularly successful in search, e-commerce, advertising, social media and other verticals. These applications have been particularly focused on predictive accuracy and often involve large amounts of data — sometimes in the region of terabytes — in fact this… Continue reading Why would I ever NEED Bayesian Statistics?