Data Analytics and AI
The world’s top provider of managed cloud services that are automation-driven and application-focused. We guarantee a smooth transformation process to become a data-empowered, intelligent organization.
Modernize core assets (virtualization of legacy systems, infrastructure, computing resources, networks, servers, data centers, storage, platforms, and third-party systems) and cutting-edge onboard applications to digitize operations and workflows across all departments. Migrate to the cloud with zero disruption to business as usual.
Getting Your Business Ready for AI and Data Analytics
Planning for storage, networking, and AI data requirements, among other things, is a deliberate part of building an artificial intelligence infrastructure.
Infrastructure needs for AI: Big Data Storage
The storage must scale up as the number of data increases. Effective AI applications require adequate storage capacity, IOPS (input/output operations per second), and dependability to handle vast volumes of data.
An organization’s storage requirements can be determined based on various factors. For example, sophisticated, high-value neural network ecosystems could experience scalability problems with I/O and latency. Similarly, BFSI companies that rely on instantaneous trading decisions require quick all-flash storage technology.
The nature of the source data would be another essential consideration. Will apps analyze sensor data in real time, or will they need post-processing? How much will AI applications produce data? Companies must continuously assess capacity as databases expand over time to prepare for expansion.
Networking Architecture for AI:
Another crucial element of implementing AI is networking, which necessitates frequent improvements. Enterprise networks must be highly scalable with high bandwidth and low latency because deep learning algorithms rely heavily on communications. Automation should be used by businesses whenever possible.
Intent-based networks that can foresee network needs or security concerns and respond in real time are created by software-defined networks (SDNs) powered by machine learning.
Workloads for Artificial Intelligence:
Powerful computing hardware, such as CPUs and GPUs, are essential for AI infrastructure. A CPU-based environment can handle basic AI workloads, but deep learning requires enormous data sets and scalable neural network techniques. Companies must use GPUs to help with that for them to optimize their data center infrastructure and increase power efficiency.
Getting ready for AI data:
Organizations must take this into account in several ways. Storage, processing, and administration of the information used or produced by the AI are all included in this. Data scrubbing, also known as cleansing, is one of the crucial procedures. This includes eliminating data from an incorrect database, lacking, formatted incorrectly, or duplicated.
With AI, data quality is essential. Using automated data cleansing tools to check data for inaccuracies using rules or algorithms is crucial because the quality of the output depends entirely on the quality of the input.
Data Governance for AI:
Data access management is critical and requires adequate procedures to only share information with those requiring it. Data management techniques make sure that users, machines, and other endpoints may access data quickly and easily without sacrificing security. This calls for appropriate data access restrictions, including IAM, data encryption programs, and others.
Optimize or Begin using Data Analytics and Artificial Intelligence
We can assist you in overcoming any of the issues mentioned above.
- Assessment, consultation, and support services that are thorough and domain-specific to assist in integrating cutting-edge analytical techniques with data modeling and designing.
- Data archiving by industry standards for automated, effective data cleansing and profiling.
- To establish a universal data architecture and streamline the gathering, processing, and analyzing data from various sources and IT ecosystems.
- Complete data management and ingestion across all cloud environments. Modernize the cloud and all linked landscape workflows by deploying cloud-native data analytics and AI capabilities.
- Use cutting-edge automation and RPA solutions to enhance crucial business outcomes. Maximize advantages while minimizing expenditures. Remove duplications or highly efficient and optimized business processes instantly.
- Use Big Data solutions to identify processes, techniques, and systems that are resource and expense intensive. Fix inefficiencies and boost output to cut costs across the board for the company.
- Infrastructure health needs to be managed and monitored in real-time to avoid unexpected outages and calamities.
- Obtain global real-time visibility into corporate operations, workflows, systems, processes, apps, and performances through logical analytical dashboards and astute reporting. A single pane of glass provides clever insights for wise decision-making.
- DevOps-based development, single SLA services up to the application logic layer, and testing frameworks.
- Clearly defined ETL tools and services for data engineering, modernization, project management, and tool integration.
- Security with SIEM-SOAR, MDR, EDR, SOC, threat intelligence solutions, and advanced static and dynamic dataflow monitoring.
- AI and data governance is seamless, compliant with regional, national, and industry standards, and uses the most recent methodology.
- To deploy advanced AI products and services, seamlessly solve all infrastructure-related network, platform, data storage, and management issues. To implement AI locally, across remote ecosystems, and in edge contexts, embrace pre-made models and libraries.