What does it take to deliver a machine learning (ML) application that provides real business value to your company? Once you've done that and proved the substantial benefit that ML can bring to the company, how do you expand that effort to additional use cases, and really start to fulfill the promise of ML? And then, how do you scale up ML across the organization and streamline the ML development and delivery process to standardize ML initiatives, share and reuse work and iterate quickly? What are the best practices that some of the world's leading tech companies have adopted? Over a series of articles, my goal is to explore these fascinating questions and understand the challenges and learnings along the way.

Drawing a parallel with API -- phenomenon, this was natural, but confusing at the same time.

In May this year, Gartner predicted global IT spend would fall 8% over the course of the year as business and technology leaders refocused budgets, prioritizing things like cloud collaboration solutions and cybersecurity. We might be prone to think investments in technologies like artificial intelligence (AI) and machine learning (ML) have been shelved for now. But a new report from Algorithmia suggests that's not the case. Not only has the upheaval of 2020 not impeded AI/ML efforts that were already underway, but it appears to have accelerated those projects as well as new initiatives. A key takeaway from the blind study, which included 403 business leaders involved in machine learning initiatives at companies with US$100 million or more in revenue, is that enterprise IT departments are increasing machine learning budgets and headcount despite the fact that many haven't learned how to translate increasing investments into efficiency and scale.

This post was previously published on LinkedIn Pulse. Machine learning becomes the NEW NORMAL as more and more companies are embarking in such projects; therefore, it's important to distinguish what's hype (i.e. Below you can find five such gaps which will allow you to plan and execute your machine learning journey in a safe manner, with valuable outcomes for your business. The reality is that companies don't have machine learning problems. There are just business problems that companies might solve using machine learning.

To bridge the gap between the data we're collecting and the way organizations interface with it, we need to address some uncomfortable realities. As we step into the next decade, there's a growing sense – almost an inevitable momentum – that we're headed towards a golden age of AI. Over the past year, we've witnessed incredible advances in applying artificial intelligence techniques to image recognition, language processing, planning, and information retrieval. There are more amusing applications, too, including one team teaching AI how to craft puns. See also: Will the Consumerization of AI Set Unrealistic Expectations?

Creating and deploying machine learning (ML) models supposedly takes too much time. Quantifying this problem is difficult, not least because there are so many job roles involved with a machine learning pipeline. With that caveat, let us introduce Algorithmia's "2020 State of Enterprise ML." Conducted in October 2019, 63% of the 745 respondents have already developed and deployed a machine learning model into production. On average, 40% of companies said it takes more than a month to deploy an ML model into production, 28% do so in eight to 30 days, while only 14% could do so in seven days or less. We believe Algorithmia's estimate is much closer to reality than that reported in a Dotscience survey from earlier in the year that reported 80% of respondents' companies take more than six months to deploy an artificial intelligence (AI) or ML model into production.