What We Do
Data & Machine Learning Operations
Data is a critical asset that is often underutilized and misunderstood. Unlock your data and enable your entire organization through modern DataOps and MLOps.
Data Ops and ML Ops is a methodology to drive the cost and time of data engineering changes down as close as possible to zero. This is accomplished through best practices and automation to manage the full data lifecycle. This includes ingestion, warehousing, transformations, access, discovery, analytics, validation, preparation and feature engineering. It also covers model creation, testing, explanation, serving and operations. This is similar to the DevOps movement, with an emphasis on data.
Furthermore, the demands of data have extended beyond analytics and
discovery into automated action. Companies used to rely on software to
drive automated business actions. Software is created by coding a
process that takes inputs and computes outputs. Machine learning takes a
slightly different approach: you already have the inputs and outputs, so
you let computers build software (train models) that transforms the
inputs into outputs. So it is a natural extension to combine DevOps
practices with your data and ML strategy.
Why we do it
Data is a critical asset and if used correctly, can drive your business
further. Unfortunately there are a myriad of challenges with data
governance, data platforms and the hidden operational challenges:
- Data is growing quickly (>30% CAGR)
- Demands of data are increasing (growth of data sciences, analysts, ML
- Increase in regulatory complexity that governs how data is collected,
processed and used (GDPR, COPPA, HIPAA, GLBA, and now various states
in different countries: CCPA, CPRA, CDPA, CPA, etc)
With the data and ML landscape changing so rapidly, anything that requires manual effort or one-off operations increases costs non linearly. For this reason, companies that do not modernize their approach to data are so busy trying to manage data they miss on the opportunity of truly using their data.
If the cost of change becomes insignificant, then it drives faster experimentation and innovation without losing understanding or control over your data and ML related assets. At Qarik, we focus on building fully automated environments that take complexity out of handling data. This platform provides self service data capabilities allowing your centralized data team to focus on the platform and data enablement throughout your organization. This empowers teams who own and create data throughout the organization to expose their own data as reliable products (batch and streaming) that can be consumed internally and if desired, externally, to drive additional revenue streams. The platform allows you to handle all these use cases with complete visibility, control, protection and monetization.