ML Readiness
Do you have the quality and volume of data necessary? How do you know you need ML over traditional analytics? What is your data strategy? Have your model prototypes shown causation or correlation?
Assess how close your business is to being able to build reliable ML products and identify concrete steps needed to be successful.
Data Enrichment & Augmentation
Are you short of data or suffer from low quality datasets? Enrich your data with third-party sources. Are you short of training data? Artificially grow your training dataset by creating modifications of existing data.
Data Strategy
Investing in Machine Learning before the strategy is a common mistake. Design your data-management activities to support an overall strategy. Chart the course for reaching your goals.
Exploratory Data Analysis (EDA)
With an Exploratory Data Analysis, you can turn an almost unusable dataset into a completely usable dataset, and in the process, identify the relationships in the data that can power Machine Learning innovations.
Cloud Infrastructure for ML
Do you have infrastructure capable of hyper-parameter optimization, model evaluation, automated training and retraining, version tracking and governance, or model and drift monitoring? Is your data centralized, clean, and ready for ML?
A technology backbone that provides you with all the capabilities required to successfully scale and operate an ML environment. We can develop your whole environment or lead specific components.
Data Pipelines
Make data more organized, useful, and accessible from the instant it is generated. Stream in real-time or batch upload from source to destination. Perform ELT or ETL. Achieve unlimited scale with Google Cloud Dataflow.
Data Platform for ML
Consolidate data in various formats, stored in various locations, with varying update frequencies to democratize access and produce the volume and quality of datasets required for machine learning.
ML Architecture
Cloud architectures to support the needs of production ML including model development, data processing, automated training, model deployment , workflow orchestration, artifact management, and model monitoring.
Scale and Operate ML
MLOps is a very expensive and multidisciplinary challenge to solve in-house. You need Cloud Architects, Data Scientists, Data Engineers, and ML Engineers. Even once you have all this talent in house, it's almost always the first time these roles are coming together to work on the MLOps problem. No SaaS tool or single hire solves for this.
We provide long-term strategic and operational support for ML Ops or Cloud environments by providing small special ops teams through a cost effective monthly pricing model.
CloudBench
Certified Google Professional Cloud Architects are the most scarce and expensive IT role. Gain unlimited, on-demand access to our growing "bench" of GCP architects. Annual cost is less than (1) in-house hire. Paid monthly.
DataForce
A DataForce is a specialized MLOps team hired to lead your data and ML initiatives. Teams are staffed with proven expert leaders and engineers. Service includes execution and transformation. 50% savings vs in-house hire. Paid monthly.
