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Data Science for Startups: Data Pipelines – Towards Data Science

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You can find links to all of the posts in the introduction. Building data pipelines is a core component of data science at a startup. In order to build data products, you need to be able to collect data points from millions of users and process the results in near real-time. While my previous blog post discussed what type of data to collect and how to send data to an endpoint, this post will discuss how to process data that has been collected, enabling data scientists to work with the data. The coming blog post on model production will discuss how to deploy models on this data platform. Typically, the destination for a data pipeline is a data lake, such as Hadoop or parquet files on S3, or a relational database, such as Redshift. There's a number of other useful properties that a data pipeline should have, but this is a good starting point for a startup. As you start to build additional components that depend on your data pipeline, you'll want to set up tooling for fault tolerance and automating tasks.


Productizing ML Models with Dataflow – Towards Data Science

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One of the key challenges I've faced in my data science career is translating findings from exploratory analysis into scalable models that can power products. In the game industry, I built several predictive models for identifying player churn, but it was always a struggle to get these models put into production. I've written about some of the processes used to productize models at Twitch, but each product team required a unique approach and different infrastructure.


Taming Machine Learning on AWS with MLOps: A Reference Architecture

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Despite the investments and commitment from leadership, many organizations are yet to realize the full potential of artificial intelligence (AI) and machine learning (ML). Data science and analytics teams are often squeezed between increasing business expectations and sandbox environments evolving into complex solutions. This makes it challenging to transform data into solid answers for stakeholders consistently. How can teams tame complexity and live up to the expectations placed on them? There is no one size fits all when it comes to implementing an MLOps solution on Amazon Web Services (AWS).


Machine learning (ML) projects: 5 reasons they fail

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You don't have to look far to see what's at the root of enterprise IT's enthusiasm for artificial intelligence (AI) and machine learning (ML) projects – data, and lots of it! Data, indeed, is king across a range of industries, and companies need AI/ML to glean meaningful insights from it. HCA Healthcare, for example, used machine learning to create a big data analysis platform to speed sepsis detection, while BMW used it to support its automated vehicle initiatives. While AI/ML can bring tremendous value to businesses, your team will first have to navigate around a common set of challenges. Get the eBook: Top considerations for building a production-ready AI/ML environment.


Method is all you need: 7 mistakes to avoid in Data Science

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Once upon a time, data science was valuable only for a handful of Big Tech companies. Data science is now revolutionizing many "traditional" sectors: from automotive to finance, from real estate to energy. Research by PwC estimates that AI will contribute over 15.7 trillion US dollars to the global GDP by 2030 -- for reference, the GDP of the Eurozone in 2018 was worth 16 trillion dollars [1]. All businesses now perceive their data as assets and the insights they can gain as a competitive advantage. Yet, more than 80% of all data science project fails [2]. Each failed project fails for its own peculiar reasons, but, in three years of experience, we noticed some patterns.