5 Challenges Associated with a rise in ML usage in 2020

ML, data scientists, data science, deployment, optimization, business, customer experience, customer insights, enterprise
5 Challenges Associated with a rise in ML usage in 2020

In 2020, the majority of companies will focus on utilizing ML in a big way. However, to reach advanced stages of adoption, they must overcome an array of obstacles.

The latest report from Algorithmia highlights the challenges associated with increased ML usage in 2020. The report, titled “2020 State of Enterprise Machine Learning”, is based on a survey of 745 tech professionals. It determines how companies plan on deploying ML and the significant issues they face for the same in the new decade.

4 Things CEOs need to watch out in the Evolving Digital Era

Scaling and budgeting are two of the main challenges associated with ML deployments, found the report.

  1. Struggles with scaling ML

Forty-three percent of respondents said that scaling models was the biggest challenge for ML, up from 30% in 2018. As per the report, the problem was attributed to decentralized organizational structures that result in tooling and programming language friction during scaling.

Enterprises need to realize that teams need to possess different skill sets and then recognize that the frameworks advance at a faster pace. The report offers a solution to create innovation hubs within companies that are dedicated to innovations in ML.

  1. Increase in demand for data scientists

Technological advances have resulted in organizations generating more data, thereby resulting in the need for more data scientists. As ML will have widespread adoption in the future, there will be an increase in demand in 2020 as well. The report found that nearly 60% of companies will employ between one and 10 data scientists.

Over 50% of the companies have at least one ML project in the pipeline, but the deployments are expected to double by the end of 2020. Companies will witness new data science job titles such as AI operations, ML engineers, developers, and architects, among others.

Data Privacy Secrets that Facebook and Google Don’t Want to Disclose

  1. Determining the use cases for AI

The report also looked at what companies are expecting from ML deployment. The top three use cases of ML included reducing costs (38%), creating customer insights (37%), and enhancing customer experience (34%). Cost-cutting is a primary focus of medium to large companies, while small companies are more focused on improving the customer experience.

  1. Evaluating the need for ML deployment

In 2020, ML projects will still be in nascent stages in companies. As per the report, 21% of organizations would evaluate use cases, while 20% noted they are early-stage adopters in ML production.

Twenty-three percent of respondents said they would be working with models in production, while 22% would be starting to develop models. The majority of teams would go into ML deployment without understanding the final result. It is essential to understand what business optimization will look like.

AI And Automation Will Plug Gaps in Cybersecurity

  1. Delay in ML deployment

A lot of times, organizations delay ML deployment. According to 20% of companies, they take more than 90 days to deploy just a single ML model, the report found. One of the main reasons for the delay is because the ML projects are new to the data scientists. In the case of larger companies, the road to deployment is even longer, mainly due to more approvals needed.