Is your data under control?
Data Governance, Data Pipelines & MLOps
When your data lacks proper monitoring, it’s probably not in the best shape. And without someone owning the responsibility, the issues will linger.
Data quality is a vital requirement for successful artificial intelligence and machine learning adoption. That’s why we’re here to help you regain control of your data.
How artificial intelligence accelerates the whole process
What is data governance?
Data governance usually includes four main directions leading towards data being highly available, usable, secure and maintains robust integrity. While data governance has traditionally been associated with larger corporations, it’s becoming increasingly important for SMEs and startups to prioritize and establish proper data governance practices. This is essential for these businesses to successfully implement customized AI/ML solutions.
What problems do we usually resolve?
- Data quality, accessibility and fragmentation related issues
- Underused data, not leveraged for internal insights or monetization purposes
- Datasets validation, cleansing and augmentation prior to utilizing advanced AI/ML solutions
- Data management processes integration to ensure proper data quality for the future usage
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How DataOps impacts data governance?
Adopting DataOps principles in daily operations helps to establish solid foundations for leveraging on the data using artificial intelligence and machine learning.
Data pipeline development
Whether complex or simple data pipelines are forming a backbone of your data management strategy and AI Adoption Framework, data pipelines are of use in companies of any size.
What are data pipelines?
Primarily it’s about consolidating data from multiple sources into a single data store (data lake, data warehouse) to build single source of truth and foundation of any data-related activities further on.
Data pipelines monitoring
If the data is not monitored, it’s most likely in a bad shape. Proper monitoring of data pipelines and automated jobs are essential for consolidated, usable and accessible data.
Scalability planning
As the business grows, data handling infrastructure might become a bottleneck for further implementation of advanced AI/ML Solutions. Let’s think together about future and scalability.
Machine Learning Operations (MLOps)
What is MLOps?
MLOps, also known as Machine Learning Operations, is a strategy for effectively overseeing the entire existence of a machine learning model, which includes its training, optimization, routine utilization in a production environment, and eventual decommissioning. It covers the whole lifecycle of Machine Learning integration.
What problems do we usually resolve?
- Model deployment and scaling
- Version control and collaboration
- Model monitoring and maintenance
- Reproducibility and compliance
- Scalability and resource management
Once data is well managed and data pipelines serve its purpose, it's time to embark on MLOps automation journey
01 Data pipelines automation
02 Iterative experiments
03 Evaluation
04 Versioning and deployment
05 Scaling
By automating the tasks of gathering, labeling, and tracking data versions, teams can smoothly progress through iterative experiments and AI/ML research phases without any interruptions.
MLOps makes the experimentation phase more efficient, a necessary step in creating reliable and effective models.
The process of iterative evaluation and taking actions based on the results can be quite time-consuming, so it's advisable to automate the technical aspects involved.
To control maintenance costs and enhance overall solution reliability, it's crucial to implement automated model retraining and deployment.
As your project expands, the need for computing power and infrastructure naturally grows as well. MLOps aims to maintain performance and enable easy scalability of the solution to meet these evolving requirements.
Discover why customers choose Altamira
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