Model Deployment

Deploying a machine learning model, known as model deployment, simply means to integrate a machine learning model and integrate it into an existing production environment (1) where it can take in an input and return an output.

Model Deployment

amigodata Has Simplified How Enterprises Manage and Store Data
On average we use no more than 5% of the information that’s stored
locally on our device’s. An eye opening fact is no more than 2% of
what’s stored remote or “in the Cloud”is used by people. Yes these
are average numbers and in some cases the percentage is slightly

There are two reasons we developed our solution.
1. The amount of energy that technology requires keeps increasing
2. The huge amount inefficiencies in data storage and the process
to save move and recall files.
“For example information we maintain on storage devices remains in
an idle mode, that means the device is powered on, but using power
to maintain data. Storage devices are operated in this manner so
files regardless of when they were last used are retrievable”.

Phase 2: Is to deploy our 30+1 data management and storage methodology. This is where we change the way your data is stored, with not only zero loss of recall speed, but actually improved response time as the data is better managed.

We have re-imagined how data is stored.
• 30+1. when a file has not be accessed in 31 days its moved to a device that will store the file while its turned off aka cold storage.
• Using business rules we make the network believe that the powered down device is still on.
• Our Business rules engine will then provide the operating system
network specific commands for locating, identifying and retrieving
information from a cold storage device.
• The solution was developed to use device backup snap shots for
devices in place of a traditional file directory.
• The solution is installed in real time with zero disruption.
• We monitor the energy savings using any third party device/rack/
row remote monitoring solution so we can get a clear picture of
the total energy savings amigodata specializes in streamlining the
data storage process. The process we have developed is one that
helps data centers reduce the energy used for data storage by no
less than 50%.

Phase 2 Activities;
• Deploy our proprietary data management rules to manage the
flow of information stored on the network. (30+1)
• Move data in accordance with any rules or regulations and
unique enterprise polices for stored information. amigodata rules
are customized for each enterprise inserted in native language
and modified in real time. “Zero down time to install”.
• After amigodata’s data management plan is in place, data will
have been moved and machine’s that are deemed “Vintage” will be removed.
•We then begin addressing the cooling for the data center with an IoT
solution that adds efficiency into any cooling system, further reducing the amount of energy used for cooling the data center by an additional 30%.

Post 30+1 Results

Once we have completed our penetration test and implemented our 30+1 file organization structure. We will be able to have a much clearer idea of what possibilities exist for your data center’s ability to reduce power use as compared to productivity. We will be able to give you a clear plan of action that is suited specifically to your data center, its location, cost of power, climate, distance from end-users, and main use of purpose. At this point, you can plan out how to bring your data center to optimal performance at optimal efficiency and how long it will take with the budget you have available. It’s easy to just give a complete list of equipment and upgrades that would get you there, however, we understand your running a business and budget constraints are a reality. So our goal is to get you there as quickly as we can while providing a positive effect to your bottom line while doing so.

“With just over 300 locations (337 to be exact), London, England has the largest concentration of data centers in any given city across the globe.”