Data Engineering
Building and optimizing data pipelines; integrating data from multiple sources for analysis and machine learning.
Data Analysis and Planning
Evaluating data needed by AI models, planning data collection, selecting storage technologies, and establishing an update strategy.
Claritype
Saves 80%+ time for Analytics and AI projects, by providing advanced capabilities that enable a company to leverage industry-specific universal schemas and start AI experimentation by Day 2, bypassing the need for complex and time-consuming mapping analysis of the data scattered across ERP and Analytics systems.
Once the AI models are defined and the business benefits are confirmed, the exact data requirements for the models will be specified. Claritype technology then analyzes the company data, identifies gaps with the universal model, and helps set up data pipelines.
Data Acquisition & Pipelines
Processes and technologies for acquiring data from multiple systems, cleaning, processing, and storing it in schemas and standards suitable for AI model use.
Scikit-Learn
TensorFlow Extended
MLflow
Kubeflow
Open Source
Provides a way to easily string together operations on a dataset into a coherent pipeline. These open-source tools make it possible to consistently perform the same cleaning and preprocessing actions on both the training and inference data.
Apache Spark
Open Source
Popular and stable OpenSource framework for large-scale data processing.
Data Storage
Secure vault for the information that fuels your intelligent models. It provides the foundation for growth, ensuring your AI has the data it needs to perform at its best.
Databricks Delta Lake
Snowflake CDP
Widely used data clouds featuring tools tailored for AI data storage. These platforms can run on major hyperscalers like AWS, Azure, and GCP, offering data lake solutions that meet most data acquisition, processing, and storage needs.
Features stores such as Feast and Hopsworks store cleaned, modified and created data so that it's ready for evaluation, improving the performance and accuracy of your model.
Features stores such as Feast and Hopsworks store cleaned, modified and created data so that it's ready for evaluation, improving the performance and accuracy of your model.
Programming Languages
MySQL
Postgres
Amazon RDS
Amazon Redshift
DynamoDB
AWS Elasticache
Memcache
Elasticsearch
ClickHouse
Hadoop
Cassandra
ScyllaDB
Model Development & Training
Model Selection
Pre-trained models for industry-specific use cases, standards and protocols.
IBM Watson
Salesforce Einstein
Google Cloud's AutoML
Offer customized solutions designed to meet the specific needs of various industries. These platforms provide advanced capabilities for developing and deploying industry-specific AI models, with quick RAG enrichment, tuning and optimal performance.
Linux Foundation
Open Source
We also use and follow updates for OpenSource industry-specific models. New models are coming daily for most general use cases and protocols, and we make it our business to keep up with them.
We are deeply involved in the Linux Foundation AI & Data Generative AI Commons, and through our chairmanship of the Models and Data workstream, we are deeply involved in the Model Openness Framework (MOF) and Model Openness Tool (MOT). The MOT enables you to know immediately what it is you're actually getting and whether a supposedly open source model actually is open source, or whether it is instead an example of "openwashing."
We are deeply involved in the Linux Foundation AI & Data Generative AI Commons, and through our chairmanship of the Models and Data workstream, we are deeply involved in the Model Openness Framework (MOF) and Model Openness Tool (MOT). The MOT enables you to know immediately what it is you're actually getting and whether a supposedly open source model actually is open source, or whether it is instead an example of "openwashing."
AI/ML Models Development
Developing machine learning models using leading frameworks.
TensorFlow
PyTorch
SKLearn
Developing AI models requires a robust framework. These tools perform efficiently and effectively in production and are often the default choice for developers due to their extensive support and robust capabilities.
Generative AI
Developing and deploying advanced generative models; leveraging AI to create content, art, and interactive experiences.
CloudGeometry continuously reviews new updates and compares both open-source and commercial LLM options for each business case.
Llama 3
Granite
Gemma
OLMo
Stable Diffusion
Prominent open-source tools for building and deploying generative AI models.
OpenAI
Amazon Bedrock
Midjourney
Offer comprehensive commercial platforms for generative AI apps. These platforms are often preferred by enterprises seeking to harness the full potential of generative AI for their business needs.
MLOps Tools
Implement and manage machine learning workflows; ensure model reproducibility, versioning, and monitoring in production environments.
Model Deployment & Management
Ensuring a stable production deployment, with ongoing measurement and enhancement of model performance and scalability through continuous tuning and improvements.
KubeFlow
MLFlow
MLRun
Widely used open-source tools that facilitate various aspects of the MLOps lifecycle, including experiment tracking, model deployment, and orchestration. CloudGeometry actively contributes to and fully supports these open-source projects, ensuring smooth and efficient workflows.
Iguazio
Weights and Biases
Offer robust commercial platforms that provide extensive capabilities for managing and scaling machine learning operations.
