Obtaining high-quality data is one of the most pressing challenges facing AI teams today. Aya Data collects high-quality data via web scraping, manual collection, and exclusive partnerships in the medical, agricultural, and geospatial industries.
Ask us about our off-the-shelf data library.
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Alongside a substantial off-the-shelf database, Aya Data has over 150 data acquisition partners covering
industries from healthcare to agriculture, in data formats ranging from DICOM to LiDAR.
The volume, format, and specificity of data required for machine learning projects is problematic, especially when a model requires a large sample of high-variance, high-dimensionality data.
In addition, collecting quality data while navigating privacy and use laws such as GDPR is tricky, especially when dealing with potentially personally identifiable information (PII). Using novel data collection techniques to obtain high-quality, compliant data is imperative to training the next generations of machine learning models.
Aya Data uses three primary strategies to help
clients build and prepare high-quality datasets.
Data collection involves retrieving data from a business’s internal systems and databases, pulling data from open and public datasets, scraping web data, collecting physical data from the environment, and creating entirely new, unique data.
Our data collection techniques include:
Aya Data can procure specialist data from a curated list of partners,
providing you with the data you need to build case-specific models, for
instance:
Data Cleaning
Data cleaning requirements vary from case to case. Aya Data will identify and correct errors and inconsistencies. This process involves fixing typos, handling missing values, removing duplicates, and standardizing formats. It’s necessary to convert raw data into an easily consumed format through encoding, scaling, and normalization.
Data Splitting and Sampling
Training sets are split into training, validation, and testing sets. Sampling techniques ensure the model is trained and evaluated on a representative sample.
Data Augmentation and Feature Engineering
In cases where data is limited, we can augment datasets to artificially increase their size and/or dimensionality and variance. New data can be generated through rotation, flipping, scaling, noise injection, pitch shifting, etc.
Our four pillars of commitment to our clients are based on years of experience of what gets
models into production the fastest and with the best results.
Our four pillars of commitment to our clients are based on years of experience of what gets
models into production the fastest and with the best results.
Our four pillars of commitment to our clients are based on years of experience of what gets
models into production the fastest and with the best results.
Our four pillars of commitment to our clients are based on years of experience of what gets
models into production the fastest and with the best results.
We follow the highest standards of data security and are GDPR and SOC 2 compliant. For sensitive projects, we
provide dedicated high-security Clean Rooms.
The only way to exceed expectations is to understand them in real-time. Effective communication is vital to effectively
complete projects which is why you will always have an open line of communication with us.
Quality is defined by you and delivered by us. Once KPIs are set, we iterate our workflow to deliver the results that you need to get the
most out of your mode.
Delays cost money so efficiency is our highest priority. We operate with 20% slack at all times to ensure you
have the data to meet your deadlines.
Harnessing AI and ML in medical imaging enables experts to direct their time and skills toward higher-level tasks such as treatment and research, rather than time-consuming diagnostics. Speeding up diagnostic processes helps streamline the entire healthcare system, ensuring patients get timely treatment.
The Client is a pan-African insurance company that wishes to utilize cutting-edge tech solutions to verify insurance claims. They assigned their in-house data science to develop a computer vision model that would detect and identify damage on vehicles.
Client X is a leading AgTech company focused on improving agricultural productivity and sustainability through cutting-edge technology solutions. Their aim was to have their in-house data science team develop a computer vision model that would detect and identify weeds.