With the increase in digitization, technological advancements, and data-driven culture, organizations are adopting agile software development practices. This paved a way for ‘XOps’ that improves business operations, efficiencies, and customer experiences by enabling teams to collaborate and communicate better while stimulating automation techniques to build an effective Ops process. It ensures reliability, reusability, and repeatability while reducing duplication of technology.
XOps began with DevOps that is an amalgamation of development and operations. According to Statista, DevOps practices across organizations grew 17% in 2018 against 10% in 2017. Like DevOps, various types of Ops such as DataOps (data), MLOps (AI models), ModelOps, Platform Ops for AI, and AIOps (analytics insights) are cross-functional endeavors focused on continually improving efficiency, and processes. Organizations are now leveraging DevOps for operationalizing their data analytics and AI architectures.
Why is there a need to operationalize AI?
As organizations embark on their digital transformation journey, CIOs are faced with daunting challenges such as:
HyperSense MLOps
Machine Learning operationalization (MLOps) is a practice that aims to make developing and maintaining production machine learning seamless and efficient. It streamlines the deployment, operationalization, and execution of ML models alongside the ML pipeline. It is where experimental data science meets an enterprise production environment where business value is created.
Establishing MLOps introduces AI models into apps a more-tuned, scalable, and successful process. AI models are traditionally deployed in a one-off fashion, and data scientists and data engineers often lack the skills to operationalize models into production environments. Application integration, model monitoring, and tuning, and workflow automation are afterthoughts. Therefore, it makes sense to bring model and app development together on a data and AI platform to harness assets and intelligence.
How HyperSense MLOps helps?
- Reducing the time to market the product.
- Mitigating risks and improving productivity.
- Increasing the top line by introducing new business models.
- Improving the bottom line by improving the total cost of ownership.
- Integrating the solution within existing enterprise applications and infrastructure is hard.
- Overlapping capabilities of data analytics and AI pipelines makes it difficult to identify the right product and framework for implementation.
- Integration and maintenance challenges while moving analytics and ML artifacts from deployment to production.
- It enables augmented data transformation that drives speed and scale for various analytics use cases throughout the lifecycle of a project or an engagement.
- It assists use-case-specific analytics by combining pioneering technologies with augmented analytics, data governance, and advanced feature engineering capabilities to operationalize high-quality insights across various business lines of an organization. It increases efficiency, quality, and governance of data operations.
- It provides a degree of self-service to the data engineering team, which is unparalleled in the enterprise space giving the right people the right data at the right time of need.
- It makes it easy to consume data for organizations struggling with trivial issues like defining data engineering roles, creating enterprise rules and data pipelines. These augmented with the right people and process will ensure that the AI experiments are executed quickly on trusted and clean data.
- It helps track data lineage by providing services like data curation, metadata management, data governance, and master data management.
HyperSense MLOps
Machine Learning operationalization (MLOps) is a practice that aims to make developing and maintaining production machine learning seamless and efficient. It streamlines the deployment, operationalization, and execution of ML models alongside the ML pipeline. It is where experimental data science meets an enterprise production environment where business value is created.
Establishing MLOps introduces AI models into apps a more-tuned, scalable, and successful process. AI models are traditionally deployed in a one-off fashion, and data scientists and data engineers often lack the skills to operationalize models into production environments. Application integration, model monitoring, and tuning, and workflow automation are afterthoughts. Therefore, it makes sense to bring model and app development together on a data and AI platform to harness assets and intelligence.
How HyperSense MLOps helps?
- It is ideal for implementing and integrating ModelOps and DevOps in an enterprise environment that is either on-Prem, on Cloud, or Hybrid in nature.
- It enables models to be pushed from a data science team to the DevOps team in a regular deployment and update cycle, aligned with continuous integration and deployment (CI/CD) to suit business needs. Powered by a powerful AI Studio, HyperSense integrates with the code platform to build and scale AI to promote explainable AI.
- It automates the time-consuming portions of data science and AI; citizen data scientists can see the results of their experiments at scale faster than before.
- It facilitates collaboration between the citizen data scientist team, DevOps, and app developers, which reduces the complexity of deploying and optimizing models in production.
Learn how augmented analytics can help transform your approach to enterprise AI
Payal is a Product Marketing Specialist at Subex, who covers Artificial Intelligence and its application around Generative AI. In her current role, she focuses on Telecom challenges with AI and its potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.