1. Faster Cycles
Disjointed analytic cycles and non-optimal tools slow down the delivery of AI-enriched insights. The marriage of big data engineering platforms such as Datameer, with next-generation deep learning platforms such as Google TensorFlow, dramatically speed the delivery of AI-enriched insights by making it faster to:
- Blend and organize a data pipeline that AI-models can consume
- Create AI models using data flow graphs and more advanced algorithms
- Train models using advanced parallel architectures and GPUs
- Plug models back into data pipelines so analysts can derive new insights
- Operationalize the AI-enriched pipelines at scale with confident governance
It is not simply a matter of a closer workflow. A recent test using a standard Generalized Linear Model showed that TensorFlow models could be trained 4 times faster than Spark, and 100 times faster than R.
2. Create better models
Feeding more data to any AI model produces greater accuracy and better results. This is especially true for convolutional neural network models, which continuously learn as more data is fed through it.
Combining big data pipelines from your data lake with AI models can pump more data through the models to deliver the desired increase in accuracy. It also allows you to take advantage of data gravity, running models directly on your data lake.
By way of example, one Datameer customer expanded the amount of data they analyzed for fraud detection from 6 months to 3 years. This produced far more accurate results and identified more fraud patterns than had been previously detected.
3. Faster IT-confident Operationalization
The highly custom re-implementation process not only slows the delivery of AI-enriched insights to the business owners, but it also flies in the face of the IT team requirements, who desire security, governance and manageability.
The ability to take AI models and plug them directly into analytic data pipelines directly on your data lake facilitates faster deployment AND gives the IT teams the desired execution frameworks that:
- Ensures the data is highly secure and governed
- Allows lineage tracking and auditing to ensure regulatory compliance
- Enables IT teams to manage jobs and performance
The IT teams become partners with the data scientists and business analysts to operationalize AI-enriched insights to the business.
4. Democratization of data science work
Data scientists do tremendous work that often goes unnoticed. Bringing together a big data engineering platform with an AI platform enables data scientists to share their models with everyday business analysts who apply them to business processes.
Data scientists can share models with business analysts through TensorFlow. From there business analysts can import models into their Datameer workflows and generate AI-enriched insights that can feed everyday business processes.
Is the AI revolution ready to take off?
Taking AI models and insights data scientists have created from the labs to the business is an important step. Bringing together the big data engineering and AI platforms not only helps create faster cycles and easier operationalization, but it also unites data scientists, business analysts and IT into a common team to deliver better results from AI to the business.