Data Science Consulting Firm Providing Data Science Services Using AI And ML Technologies To Create More Valuable Technology Solutions For Clients Around The World. We Implement Complete Big Data Solutions That Seamlessly Incorporate Data Science Technologies Such As Machine Learning, Artificial Intelligence, And Deep Learning.
The Potential Of Big Data For Healthcare Has Never Been More Realisable. Data Science In Healthcare Has Never Been More Important, From Using Predictive Analytics In Diagnosis To Revolutionising Care And Improving Patient Outcomes.
The Potential Of Big Data For Healthcare Has Never Been More Realisable. Data Science In Healthcare Has Never Been More Important, From Using Predictive Analytics In Diagnosis To Revolutionising Care And Improving Patient Outcomes.
Data Is Everything And Everywhere For Retailers In The Digital Age, With Consumers Generating 2.5 Million Bytes Of Information Every Day. It's Common Practice To Use Data To Drive Innovation Across Industries - From Next-generation Store Design To Tailored Offers, Inventory And Product Optimization, And More
AI ACME Enables Clients To Test Ideas, Track Key Metrics, Confirm Guesses, And Draw Evidence-based Conclusions That Can Be Scaled Across The Enterprise To Provide Stakeholders With A 10x Return On Investment.
With AI ACME's AI CoE as a Service, You Can Accelerate Proof-of-concept Creation, Analysis, Road Map Formulation, Use Case Prioritisation, And AI Programme Implementation By Combining The Appropriate Technologies, People, And Governance Frameworks.
Deployment Services Enable Enterprises To Implement MLops Solution Frameworks Across A Wide Range Of Business Solutions To Develop And Deploy. Ai Initiatives At Scale And Success Depend On Maintaining Customer Satisfaction.
The Process Of Finding Answers To A Given Problem Is Described By A Data Science Approach. This Is A Cycle Of Identifying Critical Behaviour That Prompts Business Analysts And Data Scientists To Take The Appropriate Action.
Like Statistical Modelling, Descriptive Analysis Provides A Clear Picture Of What's Happened In The Past, But It Stops There; It Doesn't Offer Interpretations Or Recommendations For How To Proceed.
Your business intelligence becomes actionable when you understand why a trend is emerging or why a problem has occurred. This keeps your team from making incorrect assumptions, especially when it comes to confusing correlation and causation.
The Process Of Finding Answers To A Given Problem Is Described By A Data Science Approach. This Is A Cycle Of Identifying Critical Behaviour That Prompts Business Analysts And Data Scientists To Take The Appropriate Action.
Like statistical modelling, descriptive analysis provides a clear picture of what's happened in the past, but it stops there; it doesn't offer interpretations or recommendations for how to proceed.
Your business intelligence becomes actionable when you understand why a trend is emerging or why a problem has occurred. This keeps your team from making incorrect assumptions, especially when it comes to confusing correlation and causation.
This First Step Involves A Quick Review Of The Topic By Our Data Science Team. We Examine The Information, Think About The Important Question, And Decide On The Project Goals.
After Validating The Model, We Deploy It To a Test Server So It Can Work With Actual Data. We Deploy The Model To Production If It Successfully Meets Your Business Objectives In Test Environment.
Our Data Engineers Will Carefully Review The Datasets You Provide After We Lay The Groundwork To Ensure They Select The Appropriate Dataset. To Create A Data Set, They Clean The Data And Work On Feature Development.
Once The Hypothesis Has Been Validated By The Raw Modelling, Our Data Engineers Will Do Further Optimisation And Improve The Selected Model. This Step Increases Overall Accuracy While Reducing The Energy And Time Required For Processing.
We Combine Traditional Agile Principles With The CRISP-DM Paradigm For Data Mining And Analysis. To Ensure Accuracy Of Tasks And Results, A Typical Cycle Focuses On One Hypothesis.
To Test The Hypothesis, The Data Science Team Begins Developing And Training Models Using The Processed Data. The Goal Of This Phase Is To Produce Quantifiable Results To Support The Hypothesis As Quickly As Possible.
This First Step Involves A Quick Review Of The Topic By Our Data Science Team. We Examine The Information, Think About The Important Question, And Decide On The Project Goals.
Our Data Engineers Will Carefully Review The Datasets You Provide After We Lay The Groundwork To Ensure They Select The Appropriate Dataset. To Create A Data Set, They Clean The Data And Work On Feature Development.
We Combine Traditional Agile Principles With The CRISP-DM Paradigm For Data Mining And Analysis. To Ensure Accuracy Of Tasks And Results, A Typical Cycle Focuses On One Hypothesis.
To Test The Hypothesis, The Data Science Team Begins Developing And Training Models Using The Processed Data. The Goal Of This Phase Is To Produce Quantifiable Results To Support The Hypothesis As Quickly As Possible.
Once The Hypothesis Has Been Validated By The Raw Modelling, Our Data Engineers Will Do Further Optimisation And Improve The Selected Model. This Step Increases Overall Accuracy While Reducing The Energy And Time Required For Processing.
After Validating The Model, We Deploy It To a Test Server So It Can Work With Actual Data. We Deploy The Model To Production If It Successfully Meets Your Business Objectives In Test Environment.
AI / ML FRAMEWORK
PYTORCH
TENSOR FLOW
KERAS
SCIKIT LEARN
DATA MANAGEMENT
GIT
DATA LED
MARIA DB
MONGO DB
REDIS
CLUSTER ORCHESTRATION
KUBE FLOW
SLURM
BACK END
NODE
PYTHON
GOLANG
FRONT END
REACT NATIVE
JS
REACT
SYSTEM ENVIRONMENT
DEBIAN
AZURE
CENTOS
KUBERNET
DOCKER
