AI Workbench toolsets are to enable IDEA users to perform all stages of AI/ML activities. IDEA platform intends to support the following AI/ML activities.
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Insight Foundation
Designed to manage self-contained development projects within a single, unified interface
AI & ML
AI & ML has the potential to transform all aspects of a business by helping them achieve measurable outcomes.
AI & ML
Artificial intelligence (AI) and machine learning (ML), also known as AI & ML, is significant breakthrough in computer science and data processing that is rapidly changing a diverse range of industries. Businesses and other organisations that are undergoing a digital transition are confronted with a mounting data tsunami that is both highly valuable and becoming more and more difficult to gather, handle, and analyse. New tools and approaches are required to handle the enormous amount of data being generated, mine it for insights, and act on those insights when they are discovered.AI & ML Features
A brief summary
Annotator Ground
Truth
AI Build
AI Train
AI Deploy
Reproducible MLOps
Pipelines
AI Trust
Annotator Ground Truth
It is an IDEA AI Workbench tool to enable labelling structured / tabular datasets for ML model training as well as varieties of unstructured datasets such as images for cognitive AI model training.
The indicative UI for the AI Annotator Ground Truth job specification would require the user to specify input and output data locations and media & annotation details.
This enables IDEA user to use cloud native tools as much as possible for annotating supported media types and annotation types. IDEA platform would fill the gap of unsupported media and annotation types through provisioning top picks from Open-Source Annotator tools.
IDEA supports AI Workbench for annotating most of the media types namely Image, Video, Text and Audio and their various annotation types.
Media Types
AI Build
This AI Workbench tool is to enable user to work with input data to experiment with data for visualisation, cleaning, and feature engineering to generate data ready for downstream AI/ML model training - to achieve candidate data preparation script and data preparation model.
The indicative UI for the AI Build job specification would require user to select or create desired resources in the AI Build Workspace to be able to perform experiments with data.
This service is primarily meant for pre-processing raw training data to prepare training data such that subsequent steps of AI-Train can directly consume the data so prepared.
The AI-Build job mainly consumes input data as reusable dataset across these kinds of jobs. The dataset could already exist as registered dataset in cloud native ML workspace service or AI-Build Job would need to provision its creation and registration.
AI Train
This AI Workbench tool enables user to work with input data to experiment with training AI/ML models, hyperparameters on a variety of domain problem types and standard ML frameworks to build a trained ML model.
The indicative UI for the AI Train job specification would require user to select or create desired resources in the AI Train workspace to be able to perform model training.
The interaction between IDEA platform and cloud native ML involves these components
- Cloud native ML platform used in interactive mode for experimentation through the sample code to achieve optimal training script during development phase of given AI/ML problem.
- Trained ML Model used for evaluation for the model.
- Training Script is true outcome to be taken for operationalization, i.e., it can be used for integration into AI Reproducible MLOps.
AI Deploy
Models registered in the cloud model registry workspace are supported for deploying as live endpoints for real-time inference. The capability of selecting cloud registered models from IDEA UI is provided.
The uniqueness of endpoint names is ensured. Also monitoring the status of deployment within IDEA UI is provisioned.
Reproducible MLOps Pipelines
KubeFlow Pipeline
For Azure and AWS, IDEA provides facility to register Kubeflow ML pipeline jobs. From the IDEA UI, users can navigate to the Kubeflow dashboard and create/run and monitor the MLOps pipeline jobs. It covers
- Kubeflow setup on managed and horizontally scalable cloud cluster
- Secure MLOps through tenant resource isolation, profiles, RBAC, etc
- Kubeflow Pipeline components for orchestrating key ML steps of pipeline Cloud Platform (Container Registry, Train, Model Registry, Model Deployment)
Cloud Native (GCP)
This module supports cloud native Kubeflow MLOps pipeline creation for GCP through IDEA UI. User can provide build, train and deploy input parameters through UI and create a GCP cloud native Kubeflow pipeline, which they can visualise in the Vertex AI Pipelines dashboard.
- Development Phase: Orchestrator enables Rapid ML Experimentation
- Production Phase: Orchestrator helps automate execution of the ML pipeline based on a schedule or certain triggering conditions
- Versioning of pipelines, experiments tracking ,and analytics
AI Trust
Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Model Interpretability is critical for data scientists, auditors, and business decision makers alike to ensure compliance with company policies, industry standards, and government regulations.
AI Trust is all about the interpretability of results produced by AI/ML models during training. Interpretability is essential for
- Model debugging
- Detecting fairness issues
- Human-AI cooperation
- Regulatory compliance
This AI Workbench tool enables IDEA User to work with input data to experiment with training AI/ML Models, hyperparameters on variety of domain problem types and standard ML frameworks to build an explainable ML Model.
Design for Industrialization
Service Benefits
Intelligence Service
Accelerate & modernize decision-making process through modern “Decision Intelligence Service” integrated with Enterprise Data infrastructure.AI Ready
Bring AI into the hands of business users and data scientists alike through Democratizing access to analytics-ready data.End to End
Enables industrialized end-to-end AI Pipelines to allow organizations to generate trusted real-time insights.Serverless
Production grade Serverless Reproducible AI abstracting Cloud Native ML Platforms capabilities.
Insight Foundation
An AI workbench is a customizable analytics and AI tool that allows non-data-scientists to manage, visualize, and analyze their own unique data in ways that are uniquely important to their business.
Can you afford to buy technology that solves just one or two business problems at a time? Probably not, which is why you need an AI workbench - a flexible AI tool that you can tailor to your own data, your own people, and your own business needs.
IDEA is a unified accelerators platform for Data & AI modernization. It abstracts its users away from the details of Cloud platform IaaS, PaaS, Security, Governance, etc. It implements Best Practices and Reference Architectures to fast-track AI development and industrialization. AI Workbench like all other modules are implemented as modular microservices integrated with centralized metadata based governance. It brings data from anywhere accessible through IDEA Cloud Data Foundation into AI Workbench. It helps user get started with templatized AI/ML code for wide variety of AI Domains and AI Problem Types.
IDEA AI Workbench provides MLOps capability through Kubeflow - which is one of the best industry grade MLOps platform. IDEA deployment provisions secure Kubeflow integrated with AWS services for managed and scalable MLOps from within Kubeflow control plane. It helps user get started with templatized MLOps pipeline of AI Build, Train & Deploy stages designed to run managed AI Train and AI Deploy on AWS Sagemaker for managed and scalable MLOps.
Yes, please contact the IDEA Support team.
Next Steps
To learn more about IDEA by Capgemini and
how we can help make data your competitive edge.
Visit : www.capgemini.com/ideabycapgemini
