Inefficient ML Teams
ML scientists and engineers are spending too much time on plumbing and low-value tasks, such as setting up their infrastructure and tooling, hacking around data pipelines, and building basic automation, delaying projects.
As new hardware products (Google TPUs, latest NVIDIA chips) and software products (Google AutoML) roll out, it becomes hard for decision makers to make long term decisions about ML infrastructure.
Low Hardware Utilization
Compute infrastructure is underutilized, leading to suboptimal costs and/or ML teams to battle over scarce resources.
Heterogeneity of skills and stacks
Traditional data scientists and IT managers are slowly migrating to new technologies (advanced ML and deep learning) and need to work alongside the new generation of deep learning native scientists.
Regulation and Privacy
Data is either critical and needs constraining security, and emerging regulation creates additional challenges (e.g. HIPAA, GDPR).
Human-centered design and UX
The abstract nature of many AI services and applications makes it difficult to think about interactions with humans. Given the fear of AI replacing jobs, the right UX becomes even more important to show how the two can collaborate for stronger results.
We make ML teams more efficient by setting up their infrastructure and tooling.
- Model Prediction
- Model Deployment
- Model Versioning
- Model Architecture, Model Evaluation, Model Training
- Data Labelling, Data Versioning
- Data Review and Filtering
- Data Sourcing, Data Enrichment
We enable our clients to develop networked, distributed, and collaborative robotics by asking: how can many machines collaborate to achieve a common goal?
We help you with designing a data strategy for extracting the most value from your data.
We build the right data infrastructure required for production-level AI solutions.
We train your team by building an MVP of an AI solution that suits your business needs.
We deliver production-ready AI solutions, and help your team to fine-tune and build upon it.
AI’s impact is estimated to be 7-10% of industry revenues. Our industry agnostic AI infrastructure accelerates adoption for AI Pioneers so they can benefit from this positive revenue impact.
Take our Prediction Dial survey to find out where you stack up against other industry players in AI adoption
New approach to AI
The Caliber Difference
you've built your AI team
- Productize AI
- Support internal ML team
- We build the “AI Platform”
you're building your AI team
- We take practical decision whether to use ML or DL
- We build the end to end platform
- Production-ready platform
- Applied knowledge of AI/ML tools
- We thrive on solving hard problems
- Versatile and lean
- We know what doesn’t work and are more efficient as a result
- We have built products not just algorithms/services