Embracing a Day-One Machine Learning Mindset

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Embracing an ML-first mindset helps startups accelerate time-to-market and build long-term competitiveness

Allie K. Miller, AWS, @alliekmiller  in Venturebeat

Of the many fascinating insights I get from working with successful startups across the world, one particularly stands out: machine learning (ML) and artificial intelligence (AI) are no longer aspirational technologies. I’m not alone in that notion. IDC predicts that by 2024 global spending on AI and cognitive technologies will exceed $110 billion, and Gartner forecasts that by the end of 2024, 75 percent of enterprises will shift from piloting to operationalizing AI.

Born in the cloud, most startups have the advantage to kickstart their “digital transformation” journey with less technical debt earlier in their life. They can come right out of the gate enabling a culture of innovation and acceleration by taking advantage of ML applied to what could soon become vast quantities of data to make accurate forecasts, improve their decision-making process, and deliver value to customers quickly.

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In fact, startups are uniquely positioned to take advantage of scalable compute power and open-source ML libraries to create never-before-seen businesses focused on automation, efficiency, predictive power, and actionable insights. For instance, AWS collaborated with Hugging Face, a leading open-source provider of natural language processing (NLP) models known as Transformers, to create Hugging Face AWS Deep Learning Containers (DLCs), which provide data scientists and ML developers with a fully managed experience for building, training, and deploying state-of-the-art NLP models on Amazon SageMaker. Data scientists and developers globally can now take advantage of these open-source ML models to deploy and fine-tune pre-trained models, reducing the time it takes to set up and use these NLP models from weeks to minutes.

This shift towards ML-driven efficiency is changing the way founders and creators think about getting their products and services to market. The drive to accelerate the pace of innovation through ML is fueled by access to open-source deep learning frameworks, growing availability of data, accessibility to cutting-edge research findings, and the cost-effectiveness of using the cloud to manage, deploy and distribute workloads.

My advice to founders and builders is that now is the time to build an “ML-first” business, integrating ML from day one, whether they build their own ML models or leverage AI solutions that use pre-trained models. Startups that are ML-first will be in the best position to take what we call a “Day One approach” – being customer-obsessed, focused on results over process, and agile enough to embrace external trends quickly. Getting it right the first time is less important, as experimentation and risk-taking are the root of all product growth. With that in mind, here are four ways for startups to build and grow a strategic ML-first business:  ....'