Data Science Consultancy
Innotomy offers AI and ML consulting services in following (and multi-disciplinary) domains:
- Data engineering
- Statistical learning and inference
- Machine learning and applications
- Deep learning and applications
- Natural language processing
- Knowledge graphs and graph neural networks
- Geometric deep learning
- Scientific data analysis
- Artificial scientific discovery
- Multi-modal understanding
- Explainability
- Fairness
Innotomy offers the services in various domains in the following technology areas:
- Data engineering (wrangling, cleaning, missingness and analytics)
- Statistical learning and inference
- Machine learning and applications
- Deep learning and applications
- Reinforcement learning and applications
- Natural language processing
- Knowledge graphs and graph neural networks
- Geometric deep learning
- Scientific data analysis
- Artificial scientific discovery
- Multi-modal understanding
- Explainability
- Fairness
- Causal Inference and discovery
Innotomy offers the following types of services:
- High-end consulting services (defining and validating strategy)
- AI implementations
- Teaching and research mentorship
- Training and workshops
Consultancy
- High-end consultancy
- Experience across domains for years
- Top-end technology and business perspective
- Quality and neutrality
Teaching
- Teaching subjects in both technical and management tracks
- Focus on student learning
- Use of innovative teaching tools
- Project based, hands-on learning
- Application oriented
Training and Workshops
- Conduct specialized training and workshops in specific topics
- Audience could be teachers/students in academia and senior management/engineers in corporates
- Project based, hands-on learning
- Focus on coverage, quality and acceptability
Cloud computing
Cloud computing has transformed the data science industry by making high-end computations accessible to everyone at very low cost. Innotomy has experience in using GPUs and CPUs for solving AI problems.
- NVIDIA GPus - K80, P100, Jetson TX2, RTX A1000
- Google TPUs v2
- AWS
- Google Cloud
- FloydHub
- Paperspace
- Google Colaboratory
- others
Programming languages
The languages that make data science happen!
- Python - By far the most popular. Data science library is rich with Numpy, Pandas, Matplotlib, Seaborn, Scipy and Scikit-Learn among others
- Julia - A language for high performance numerical analysis and computational science, also used as spec language in quantum computing
- R - Preferred language in machine learning and statistics. Also used generally in Genomics. Powerfully backed by Bioconductor
- JAX - a much preferred platform for AI and ML these days and executes faster!
- Swift - Need to watch out!
- C/C++ - The underlying libraries in Python/R are built using these!
- others - its a growing landscape
Platforms/ Frameworks
Popular platforms and frameworks that are used by the AI community and those used by Innotomy include:
- Tensorflow/Keras
- PyTorch
- HuggingFace
- Gradio
- Fast.AI
- MxNet
- CNTK
- AWS SageMaker
- Microsoft Azure
- doWhy
- others
Data science works with computer science, statistics and mathematics. Innotomy offers data science consultancy in following areas across domains:
- Strategy - Explore what is possible with your data and create a detailed plan
- Typical questions that are answered in this phase involve decisions about what to do, what data to collect, how to collect it, how to store it, how to protect it and how to implement the solution.
- Validation of strategy - A perfectly well thought out strategy could be created in no time but may take forever to implement. Validating proposed strategy is vital for business success.
- Typical questions that are answered in thsi phase involve detailing the insight behind the strategy, low-cost ways of testing the strategy and what do these ways tell about the strategy.
- Model development - Design and build a modern data product
- Employee training - Equip your employees, who may have diverse exposures, with a common basic understanding of technologies so that they can take the strategy and implement it for you.
- Training boosts data literacy of your teams. This is a continuous evaluation and training phase.