Machine Learning Technology: Is it Just Hype or the Secret Sauce for Better Analytics?

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Machine Learning Technology Diagram 1There’s no denying that machine learning is a hot technology right now. But why? And what is it, exactly? Machine learning employs algorithms that analyze data to find models – models that can predict outcomes or understand context with significant accuracy and improve as more data  is available.Enterprises use machine learning along with its umbrella technology, artificial intelligence (AI) to automate decisions, hyper personalize customer experiences, gather new insights, and streamline operational processes.

In fact, according to a report by Forrester, entitled “Global Business Technographics Data and Analytics Survey, 2016-2017“, in 2017, 54% of decision makers (respondents = 1,246 decision makers of firms with 1,000+ employees) reported that their company had already adopted AI for analytics. Another 19% of the respondents said they were planning to implement AI in the next 12 months.

Machine Learning Technology Diagram 2

Despite the enthusiasm, enterprises often struggle to get machine learning models built by data science teams into production applications. The problem is that existing technical architectures are poorly equipped to handle model management and the multiple data pipelines that are required to infuse machine learning into existing and new applications.

Attunity recently hosted Forrester Research Vice President & Principal Analyst, Mike Gualtieri on a webinar titled Data Architecture for Machine Learning: Five Gaps Enterprise Architects Must Fill. The goal of the webinar was to demystify the machine learning lifecycle and enumerate the architectural and data integration requirements that enterprise architects must understand to fill the gaps needed to support machine learning model training and deployment. The presenters explained how to accelerate the machine learning process, the value of machine learning for analytics and why so many companies are investing in the technology right now.

Watch the replay of this webinar to learn how to:

  • Better understand the machine learning lifecycle and architectural requirements
  • Support the machine learning model training and deployment
  • Leverage real-time data integration to accelerate machine learning data pipelines
  • Streamline operational processes and enable higher productivity
  • Use machine learning to automate decisions and discover new business insights

Once you watch this session, you’ll be able to decide for yourself if the hype around machine learning is warranted. Watch the on-demand webinar, Data Architecture for Machine Learning: Five Gaps Enterprise Architects Must Fill now.

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