A few years ago, the analytics market set their sights on AI databases. The goal was to create a purpose-built solution which could “help you better wrangle the volume, velocity, and complex data governance and management challenges associated with training ML and deep learning models to save time and optimize resources” (PCMag).
This is now what many solutions offer, but these solutions are simply papering over the cracks of why processing AI models is so demanding on both time and resource.
Brytlyt has shifted the goal posts yet again. Our unique in-database AI platform addresses the root cause of the issue rather than the symptoms. By bringing together AI and databases within one unified solution, we are bridging the long-held divide between where AI is built and trained, and where it is deployed and maintained. Our solution defines a new paradigm in database design so that AI models are serviced from data discovery to deploying and maintaining models in production, all within the database engine.
The key benefit is a 3x reduction on the amount of code needed for a given outcome and a massively simplified process to get AI into production and keep it maintained. Data engineering, data science and MLOps teams gain a 6x improvement in productivity and new users to the platform experience at least a 9x shorter onboarding time – what would normally take two weeks can be achieved in less than a day!
The benefits of a purpose-built AI database
If alternative solutions make AI processes faster and more efficient, what is the need for in-database AI?
To create a truly purpose-built AI database, the fundamental divide between AI and database functions needs to be bridged. Any other solution is simply papering over cracks. Brytlyt is treating the root cause – not the symptoms. The benefits can be found in the undeniable outcomes of using Brytlyt’s in-database AI solution:
- 3x reduction in code
- 6x improvement in productivity
- 9x improvement in onboarding
Running AI and database processes separately is highly inefficient and costly for organisations.
When compared to solutions built to train and deploy AI/ML, databases have entirely different ecosystems; from the skillsets of experts in these fields, to the technologies and processes that are performed within them.
AI models once deployed to production start to age immediately. This is because the real-world data they are consuming begins to diverge from the data they were trained on. As soon as they’re deployed into production they need to be retrained and maintained to remain effective. The retraining pipelines, new datasets and resulting updated model all need to be version-controlled.
Keeping AI models in production within this disconnected ecosystem is therefore extremely expensive without true in-database AI. This has a huge impact on time to market, and the size and skill levels of the teams required. Brytlyt’s in-database AI allows domain experts without data science expertise to gain the full benefit of AI decision support.
Breaking down the disconnect between database and AI
Brytlyt’s in-database AI is the only platform to truly bridge this disconnect at an infrastructure level.
Although many solutions claim to bring the two entities together, what they’re actually doing is connecting the libraries to bring AI functions closer to the database. While this does make certain processes quicker, the two architectures are still fundamentally separate, meaning that users still need to extract, copy, and transfer data between them.
Brytlyt has removed these additional steps by redefining database tables using tensors in PyTorch source code. This means the AI and database can communicate and share resources directly, creating a completely unique GPU database with an integrated deep learning environment.
Through a single platform, users can access Python, Jupyter Notebook, or MLFlow. They can map and maintain neural networks using only a few lines of code, and perform AI model inferencing using a standard SQL language.
The Brytlyt platform has simplified the code and syntax required, making it easier to onboard new users and more efficient to deploy and maintain AI in production.
The next step for ML and AI platforms
Altogether, this evolutionary step in data analytics technology delivers benefits for AI, deep learning and BI.
It represents a massive improvement in user experience regarding AI/ML, drastically accelerating development times, maintenance, and turnaround. In addition, the serverless nature of the platform breaks down cost barriers to GPU technologies and offers a potential 90% saving by only charging for actual usage.
With three times less code, six times increase in productivity and nine times faster onboarding, users can achieve more.
This platform is designed to make deep learning accessible to the citizen data scientist and data science experts alike. Users can now leverage the benefits of GPU for many areas of analytics, including querying billions of rows of data in milliseconds, performing ad hoc geospatial analytics in BI tools, and tabular learning through TabNet.
From category management and personalised pricing in retail, to identifying risk in finance, to accelerating genomics sequencing, a host of use cases are now available to a mass market, powered by GPU.
Explore the all-in-one platform for yourself, here.
