Enables data professionals to easily build and operationalize AI-driven data products
DataOps.live, The Data Products Company™, today announced the immediate availability of its new range of AIOps capabilities, a groundbreaking set of features that provides end-to-end lifecycle management of AI workloads from development to production. Centered around Snowflake Cortex and AWS Bedrock, these latest AIOps capabilities enable data engineers, data product owners, and data scientists to easily and quickly build and operationalize AI-driven data products with unparalleled consistency, scalability, and governance.
– To see these new AIOps capabilities in action, attend the September 26th webinar, “DataOps meets AIOps”, and/or read about operationalizing Snowflake Cortex in this blog.
In an industry where the manual creation, deployment, and maintenance of AI workloads has been the prevalent approach to date, DataOps.live’s new AIOps capabilities address the demand from businesses who need to streamline workloads. With these new AIOps features, users can now define, train, and validate models, as well as assess their fit through training loss scoring. This ensures AI models are optimized around each critical dimension, such as quality, cost and speed for each business use case.
More specifically, DataOps.live’s new range of AIOps capabilities include:
- Simplified Technical Abstractions: Quickly initiate MVPs, proof-of-technologies, and early development projects with capabilities that abstract technical complexities.
- Snowflake & AWS Integration: Seamlessly integrate with the Snowflake ecosystem of LLMs through Snowflake Cortex, and the AWS ecosystem of LLMs through Amazon Bedrock, enabling the efficient use of a variety of LLMs either as the foundation model or fine-tuned models specialized for your domain.
- Comprehensive Model Management: Automate model training, fine-tuning, and assess/re-assess quality drift over time to ensure optimal performance.
- Governance and Scalability: Drive operational efficiency with built-in CI/CD, security, and governance, and reduce operational costs by right-sizing models for specific business needs.
- Improved Data Engineering Productivity: Pre-built templates accelerate data preparation and model tailoring, enhancing data engineering productivity.
As a modern data management practice, DataOps focuses on building, managing, and operationalizing data pipelines that move and transform data, including the AI models employed in any part of that process. Successful adoption of DataOps can drive a 10x productivity increase for data engineers while ensuring data quality, governance, and pipeline efficiency.
AIOps for AI Workloads, a subset of DataOps, delivers a specific set of capabilities focused on managing AI/ML model lifecycles within these pipelines. AIOps ensures models are developed and continually assessed/reassessed against quality, trust, timeliness. and cost so they perform optimally in production environments.
According to IDC, the Artificial Intelligence (AI) Lifecycle workload category – which encompasses the entire spectrum of AI development and deployment – saw a 26.6% increase in spending year-over-year in the 12 months to May 24.
“With the launch of our new range of AIOps capabilities, we’re providing a complete foundational level of capability that boosts data engineering productivity and provides the critical capabilities needed to operationalize AI Models and Workloads within DataOps.live pipelines,” said Guy Adams, CTO at DataOps.live. “Developer productivity, model governance, model change control, and model auditability are critical as businesses make real decisions based on their AI models, and DataOps.live ensures that these elements are baked into every step as we operationalize AI Workloads.”
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