Io-Tahoe Webinar – Operationalized AI and Machine Learning: Challenges and Possible Solutions

By PRNewswire On Jul 3, 2018

Combining Artificial Intelligence, Machine Learning and Data Science to Produce Real-World Results

Io-Tahoe LLC, a pioneer in machine learning-driven smart data discovery products that span a wide range of heterogeneous technology platforms, recently announced the next in its educational webinar series entitled, “Operationalized AI and machine learning: challenges and possible solutions.”

Date & Time: Tuesday, July 3, 2018, 12 – 1 pm Eastern Time

Guest: Ciprian Jichici, General Manager, Genisoft

Why Attend? Artificial Intelligence (AI) has captured the imagination of many organizations, from small start-ups to vast Enterprise corporations. Doing AI successfully, and in a consistent fashion, can be an elusive goal, a fact which limits productivity, project success and mainstream adoption.

The workflow and tooling for data science, machine learning and artificial intelligence is still in a primitive stage. Moreover, the integration of those disciplines with data engineering and software development is even less evolved. Coordinating those three pursuits with the operational side of things – deployment, testing, monitoring, troubleshooting and retraining – is perhaps the most audacious goal of all. Does today’s toolchain allow such coordination of efforts? If not, can bespoke solutions be cobbled together, and would the outcome justify the investment?

Ciprian Jichici

Special guest Ciprian Jichici, General Manager, Genisoft will join Andrew Brust – Io-Tahoe’s Market Strategy Advisor, ZDNet Big Data Blogger and Founder/CEO of Blue Badge Insights for this live webinar. Ciprian is a software development authority, a consulting veteran and an accomplished data scientist; if anyone can envision an implementation of DevOps discipline in AI, it’s him.

Also Read: Mobvista Appoints Ramon Zhu as Vice President of AI and Machine Learning

In this Webinar you will learn:

The challenges to making data science efforts conform to software development best practices.
How repeatability and operationalization of machine learning and AI is made difficult by fragmentation in the data science technology landscape and yet is still critical to success.
About new projects in the machine learning world that are making data science more procedural and repeatable, and better-integrated with data engineering and analytics.
How software applications with embedded AI offer a on-ramp to the technology, without the operational burdens.