Analytics and Artificial Intelligence
In today’s environment, Analytics & AI is the key to automating, accelerating, and enhancing key business processes to help transform at scale and drive value.
Our Solution Capability on Analytics and AI
Aptus Data Labs has a team of advanced analytics professionals and data scientists with advanced degrees in various fields, such as computer science, statistics, applied mathematics, operational research, and business SMEs to work with our customers.
Advanced Analytics and Artificial Intelligence Solutions
Data Discovery & Management, Data Integration & Streaming, Distributed Data Lake / Data Warehouse / Analytical Data Hub
AI Solution for Industry
Consulting and Technical Services, System Integration and Deployment, Data Security and compliance ready
AI Services
On-Premise, Cloud (AWS, GCP, Azure), Hybrid Cloud
It's all about building something you can put to use
- Machine learning
- Natural Language Processing
- Computer vision
- Prescriptive Analytics
Our approach to getting started with fully automated Data Science
From data exploration to modeling to production. Aptus Data Labs offers a complete path to fully automated data science with Data Preparation, Model Building, and Model Ops. We follow the CRISP-DM process within an agile development methodology to productionize data science (MLOps) projects.
Platforms, Tools & Technologies
Aptus Data Labs is equipped with industry-leading competitive platforms, tools, and technologies. We have partnered with OEMs to build joint solutions & services to ensure the success of business use cases.
Artificial Intelligence (AI) is a branch of computer science that deals with the development of systems and algorithms that can perform tasks that would typically require human intelligence, such as natural language understanding, image and speech recognition, and decision-making. Data science is closely related to AI, as it involves the use of statistical methods and machine learning algorithms to extract insights and predictions from data.
Common applications of AI in data science include natural language processing, image and speech recognition, predictive modeling, anomaly detection, and automated decision-making. These techniques are used to extract insights and predictions from data, and can be applied in a wide range of industries such as healthcare, finance, and retail.
Challenges of using AI in data science include data quality and availability, model bias and interpretability, and ethical and legal considerations. Additionally, as AI and data science technologies evolve, it can be challenging to stay up-to-date with the latest tools and best practices.
To ensure that AI models are fair and unbiased, organizations should use a combination of techniques such as data preprocessing, algorithm selection, and model interpretability. Additionally, they should also conduct regular performance evaluations and audits to identify and address any issues with their models.
Popular AI frameworks and tools used in data science include TensorFlow, PyTorch, and Scikit-learn. These frameworks provide a wide range of pre-built algorithms and tools for data preprocessing, model building, and evaluation.
To stay up-to-date with the latest AI and data science technologies and best practices, organizations should invest in training and education for their data science and AI teams. Additionally, they should attend conferences, read industry publications, and connect with other data science and AI professionals to stay informed about new developments and trends.
Case Studies
Deriving Useful Patterns: The Business Impact of IT Analytics
Fraud Detection Analytics for Employee Expense
Converting Clicks to Sales with AI Analytics for Footwear
Unlock the Potential of Data Science with Aptus Data Labs
Don't wait to harness the power of data science - contact Aptus Data Labs today and start seeing results.
Are you planning to take your business to the next level with data science? We invite you to connect with us today to schedule a consultation. Our team will work with you, to assess your current data landscape and develop a customized solution that will help you gain valuable insights and drive growth.
