The concept of deployment in data science refers to the application of a model for prediction using a new data. Building a model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that the customer can use it. Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data science process. In many cases, it will be the customer, not the data analyst, who will carry out the deployment steps. For example, a credit card company may want to deploy a trained model or set of models (e.g., neural networks, meta-learner) to quickly identify transactions, which have a high probability of being fraudulent. However, even if the analyst will not carry out the deployment effort it is important for the customer to understand up front what actions will need to be carried out in order to actually make use of the created models.
Model deployment methods:
In general, there is four way of deploying the models in data science.
- Data science tools (or cloud)
- Programming language (Java, C, VB, …)
- Database and SQL script (TSQL, PL-SQL, …)
- PMML (Predictive Model Markup Language)
Data Operations, combines people, processes, and products that enable consistent, automated, and secure data management. It is a delivery system based on joining and analyzing large databases. Since Collaboration and Teamwork are the two keys to a successful business and under this idea, the term “DataOps” was born. DataOps’s purpose is to be a cross-functional way of working in terms of the acquisition, storage, processing, quality monitoring, execution, betterment, and delivery of information to the end-user. It harnesses the individuals’ capacities of working for the common good and business development. Consequently, DataOps calls for the combination of software operations development teams, which is also DevOps. This new emerging discipline made up of engineers and data scientists, advocates sharing the expertise of both and inventing the tools, methodologies, and organizational structures for better management and protection of the organization. The main objective of DataOps is to improve the company’s IT delivery outcome by bringing data consumers and suppliers closer.
The main aim of DataOps is to make the teams capable enough to manage the main processes, which impact the business, interpret the value of each one of them to expel data silos, and centralize them even without giving up the ideas that impact the organization as one all. DataOps, a growing concept, seeks to balance innovation and management control of the data pipeline. Besides, the benefits of DataOps extend across the enterprise. For example:
- Improves the quality assurance and through the provision of “production-like data” that enables the testing to exercise the test cases before clients encounter errors effectively.
- Supports the entire software development life cycle and increases DevTest speed by the fast and consistent supply of environments for the development and test teams.
- It helps organizations move safely to the cloud by simplifying and speeding up data migration to the cloud or other destinations.
- Helps with compliance and establishes standardized data security policies and controls for the smooth flow of data even without risking your clients.
- Supports both data science and machine learning. Any organization’s data science and artificial intelligence endeavors are as good as the information available. So, DataOps ensures a reliable flow of the data for digestion and learning as well.
Data Optimization is a process that prepares the logical schema from the data view schema. It is the counterpart of data de-optimization. Data optimization is an important aspect in database management in particular and in data warehouse management in general. Data optimizations is most commonly known to be a non-specific technique used by several applications in fetching data from a data source so that the data could use in data view tools and applications such as those used in statistical reporting.
The data optimization process makes use of sophisticated data quality tools, such as those provided by Precisely, to access, organize, and cleanse data, whatever the source, to maximize the speed and comprehensiveness with which pertinent information can be extracted, analyzed, and put to use. That enhanced availability of critical information provides businesses with significant benefits.
A logical schema is also a non-physical dependent method of defining a data model of a specific domain in terms of a particular data management technology without being specific to a particular database management vendor. In more simple terms, the logical schema refers to the semantics describing a particular data manipulation technology and these descriptions could be in terms of tables, columns, XML tags and object oriented classes.
Maximized IT availability and flexibility
Many enterprises today are moving to a multi-cloud model for their IT operations, not only to take advantage of the unique capabilities of each platform, but most importantly, to protect themselves from the effects of a cloud provider unexpectedly going offline. Because the various cloud platforms have different native storage formats and analytical tools, optimizing and perhaps compressing data before sharing it between platforms can greatly facilitate implementation of a multi-cloud strategy.
Increased ROI for IT infrastructure and staff
The infrastructure tools used for optimizing data also provide insight into the performance of the server, storage, network, and system software components of a company’s IT operations. Having access to such information greatly facilitates tasks such as planning, troubleshooting, and forecasting, resulting in more efficient use of hardware and software resources, and of IT staff personnel.
Performance that meets customer expectations
In the internet age, customers have come to expect and demand speed, accuracy, and comprehensive information from the businesses they deal with. Whether the interaction is online, by telephone, or face-to-face, customers expect front-line personnel to be able to respond quickly with accurate and pertinent information. Data optimization is often the key to providing real-time service that meets customer expectations.
Agility and flexibility in decision-making
In today’s business environment, threats and opportunities can appear with lightning speed, and a company’s very survival can depend on how quickly it reacts. The key is having timely access to good information. But pulling together data from disparate sources and formats, even with automated tools, can be a time consuming and error-prone endeavor.
Data optimization alleviates that problem by restructuring datasets and filtering out inaccuracies and noise. The result is usually a significant increase in the speed with which actionable information can be extracted, analyzed, and made available to decision-makers.
Enhanced company reputation
Poor data quality can produce inaccuracies and inconsistencies that reduce the perceived trustworthiness and utility of information that is critical to the operations of the business. The result is often the introduction of confusion, delay, and the potential for strife into transactions with customers, business partners, and even employees. The improvement in data quality brought about by the data optimization process minimizes a company’s exposure to such problems, and enhances its overall reputation.