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Operationalize the model
Once you have obtained a set of models with expected performance levels,
these models can then be operationalized for other applicable applications
to use. According to the business requirements, predictions can be made in
real-time or on a batch basis. In order to deploy the model, they must be
integrated with an open “Application Programming Interface” (API) to
allow interaction of the model with all other applications and its
components, as needed.
Deliverables to be created in this stage
A dashboard report using the key performance indicators and
metrics to access the health of the system.
A document or run a book with the details of the deployment
plan for the final model.
A document containing the solution architecture of the final
model.
Stage V – Customer Acceptance
The goal of this stage is to ensure that the final solution for the project
meets the expectations of the stakeholders and fulfills the business
requirements, gathered during the Stage I of the Data science lifecycle. The
two primary tasks that must be accomplished in this stage are: "system
validation and project hand-off".
Deliverables to be created in this stage
The most important document created during this stage is for the
stakeholders and called as “exit report”. The document contains all of the
available details of the project that are significant to provide an
understanding of the operations of the system. TDSP supplies a
standardized template for the “exit report”, that can be easily customized to
cater to specific stakeholder needs.
Importance of Data Science
The ability to analyze and closely examine Data trends and patterns using
Machine learning algorithms has resulted in the significant application of
data science in the cybersecurity space. With the use of data science,
companies are not only able to identify the specific network terminal(s) that
initiated the cyber attack but are also in a position to predict potential future
attacks on their systems and take required measures to prevent the attacks
from happening in the first place. Use of “active intrusion detection
systems” that are capable of monitoring users and devices on any network
of choice and flag any unusual activity, serves as a powerful weapon against
hackers and cyber attackers. While the “predictive intrusion detection
systems” that are capable of using machine learning algorithms on
historical data to detect potential security threats serves as a powerful shield
against the cyber predators.
Cyber attacks can result in a loss of priceless data and information resulting
in extreme damage to the organization. To secure and protect the data set
sophisticated encryption and complex signatures can be used to prevent
unauthorized access. Data science can help with the development of such
impenetrable protocols and algorithms. By analyzing the trends and patterns
of previous cyber attacks on companies across different industrial sectors,
Data science can help detect the most frequently targeted data set and even
predict potential future cyber attacks. Companies rely heavily on the data
generated and authorized by their customers but in the light of increasing
cyberattacks, customers are extremely wary of their personal information
being compromised and are looking to take their businesses to the
companies that are able to assure them of their data security and privacy by
implementing advanced data security tools and technologies. This is where
data science is becoming the saving grace of the companies by helping
them enhance their cybersecurity measures.
Data science has made the use of advanced machine learning algorithms
possible which has a wide variety of applicability across multiple industrial
domains. For example, the development of self-driving cars that are capable
of collecting real-time data using their advanced cameras and sensors to
create a map of their surroundings and make decisions of the speed of the
vehicle and other driving maneuvers. Companies are always on the prowl to
better understand the need of their customers. This is now achievable by
gathering the data from existing sources like customer's order history,
recently viewed items, gender, age and demographics and applying
advanced analytical tools and algorithms over this data to gain valuable
insights. With the use of ML algorithms, the system can generate product
recommendations for individual customers with higher accuracy. The smart
consumer is always looking for the most engaging and enhanced user
experience, so the companies can use these analytical tools and algorithms
to gain a competitive edge and grow their business.
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