...
Full Bio
Artificial Intelligence And Its Genre
286 days ago
Must Aware About The Data Mining Techniques
287 days ago
Gaining Top 5 Soft Skills To Flourish In Data Science Field
290 days ago
Automation Anywhere Join Hands With Microsoft To Advance The Adoption Of RPA Technology
479 days ago
Listed Key Characteristics Of Cloud Computing
565 days ago
List Of Top 5 Programming Skills Which Makes The Programmer Different From Others?
130908 views
These Computer Science Certifications Really Pay Good To You
128736 views
Which Programming Language Should We Use On A Regular Basis?
119631 views
Cloud Engineers Are In Demand And What Programming Language They Should Learn?
108930 views
Python Opens The Door For Computer Programming
80505 views
Follow 5 Easy Steps to Get Implement Machine Learning and Artificial Intelligence in Your Organization
Machine Learning and Artificial Intelligence are getting
hype in the technological world. Both artificial Intelligence and machine learning
uses data to predict the outcomes. Both the technology offers advantages to almost
every industry and organizations are also leveraging AI and ML technologies.
Following are the pointers that help in implementing the Artificial
Intelligence and Machine Learning within the organization:
Know how AI and ML will benefit for Organizations:
When formulating to use machine learning, the first thing
organizations must do is train lead engineers to have a solid understanding of
the technology; how it works and what advantages it can deliver, said Chris
Rijnders, CEO and co-founder, Cogisen. For example, Boeing has set up a joint
lab project with Carnegie Mellon, he said, "so that its engineers can
understand its potential impact in every aspect of design, manufacturing and
maintenance." This demonstrates how critical education should be when
applying machine learning to complex environments.
Analyze the businesses which have already implemented AI and
ML technology:
AI and machine
learning are not yet in the DIY category, said Fabio Cardenas, CEO of Sundown
AI. It's all still very technical. So, it's worth finding out what other
businesses have similar goals, and how they have addressed the issue.
Select right platform:
With Amazon, Baidu, Google, IBM, Microsoft and others all
offering machine learning platforms for the enterprise, there is no obvious
place to start. Many of these options are similarly priced, and aimed at
beginners. Check out the individual articles on these platforms in this special
feature to help you decide if one of them is right for your business.
Build healthy strategy:
Data science companies like Boxever can help businesses
deploy AI for example, by addressing a question like, 'How can AI improve
marketing?' AI could help you make predictions about what happens when
customers open an email, for example, based on previous experience. This is an
easy way to integrate AI into current operations, said Dave O'Flanagan,
Boxever's CEO and co-founder, because it helps "build trust."
We had to introduce a lot of controls on rules to be able to
allow organizations to treat the output or deploy their own strategies
themselves, O'Flanagan said, and then put their strategies alongside black box
or AI strategies to be able to get comfortable with the concept of a machine
making decisions about what kind of information to present to a customer.
Prepare an implementation plan:
Before you can get started deploying your product, you need
to think about a plan. According to Sundown AI's Cardenas, a multi-region
deployment plan using Amazon Web Services (AWS) has a detailed description for
users. Setting up the AWS infrastructure would take a few days, assuming that
the web application has been tested on such infrastructure previously, he said.
If it hasn't, you'd need to set up the web app, database and other related
infrastructure on AWS, connecting all the components, Cardenas said which could
take a week or two. Additionally, it would require constantly refining the
coding for bugs, which would call for additional deployments. Cardenas
estimated that the process for a "deploy pipeline" could take another
ten days or so.