Data Analytics Design Services

With data analytics and data visualization innovations continuing at a breakneck pace, organizations across industries are leveraging data analytics design services to transform their businesses. Whether mining data for business insights or improving customer experiences, artificial intelligence (AI), machine learning (ML) and deep learning are the tools that empower you to gain insights from the data you may already be collecting. At Cardinal Peak, we leverage in-depth experience at every stage of the data analytics journey — from initial road mapping to development and validation through release and CI/CD — to help you design, implement and refine your data analytics solution.

As an AWS Consulting Partner, we take advantage of the myriad of services, expert advice, well-architected design reviews, and development cost sharing AWS offers to provide end-to-end data analytics consulting services to our clients. By leveraging Amazon’s vast arsenal of tools — from data lakes like S3, data warehouses such as RedShift and the ML development suite of SageMaker — our data analytics experts shorten your development timeline.

Data Analytics Road Map & Design Services

Whether you’re newly implementing data analytics or simply expanding capabilities, our process begins with creating a road map. In concert with AWS, Cardinal Peak helps you develop a comprehensive strategy that outlines any near-term proof-of-concept (POC) tests necessary to refine the plan before full implementation. Through this process we come to understand your business needs along with your existing data infrastructure. Our typical road map document for data analytics consulting services includes sections on:

1. 1. Data Strategy
   2. Data Visualization
   3. Data Analytics
   4. Any Gating POCs

The roadmap prioritizes the development schedule and development path. If the development is greenfield then we can architect a serverless design that utilizes Amazon’s managed services. Or if you have a large existing on-premises system, we can containerize your code and move it to the cloud as efficiently as possible.

With the roadmap in hand, the development process starts with the implementation of the data strategy. This may include aggregating data in one location, a data lake, to make analytics and machine learning processing efficient. It may also include developing purpose-built data warehouses. Our goal is a well-architected, modern data strategy that supports easy data movement and provides unified governance and access control.

Machine Learning Consulting

Our machine learning (ML) practice includes both initial development and ongoing ModelOps. Step 1 of our development process includes architecting and developing the data strategy, as described above. Step 2 is the initial model development and training. In Step 3 (ongoing), the ModelOps team continuously improves the model(s) to be more accurate and to run faster.

To enhance your connected devices with embedded machine learning at the edge, check out our blog post on Harnessing TinyML to Revolutionize IoT Development.

Steps to Develop Machine Learning Models

1. Implement Data Strategy	2. Build & Train Initial Data Models	3. Ongoing Tuning in CI/CD Environment
Using the data strategy, existing data silos are combined on the cloud Data lakes and purpose-built data warehouses are enabled for data analytics	Develop the initial machine learning data models Train the initial ML data models	Ongoing tuning by the ModelOps team Continuous effort for improving model accuracy and efficiency
Tools & Services: Amazon S3, Redshift and others	Tools & Services: SageMaker – Data Wrangler, Feature Store, Clarify and more	Tools & Services: SageMaker – Clarify, Debugger, Pipeline, etc.

Our data analytics design experts leverage SageMaker Studio for model creation and refinement. With SageMaker, we capitalize on our data strategy to easily connect and load data from S3 and Redshift. SageMaker Studio is composed of a series of tools that help with the entire lifecycle of ML models including data prep, model build, model training, deployment and ongoing refinement. Some of the SageMaker Suite tools we utilized include: