Is your data under control?
Data Governance, Data Pipelines & MLOps
Data quality is a key requirement for successful adoption of artificial intelligence and machine learning. 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 prioritise and establish proper data governance practices. This is essential for these businesses to successfully implement customised AI/ML solutions.
What problems do we usually resolve?
- Data quality, accessibility, and fragmentation-related issues
- Underused data, not leveraged for internal insights or monetisation purposes
- Datasets validation, cleansing, and augmentation before utilising advanced AI/ML solutions
- Data management processes integration to ensure proper data quality for future usage
Altamira is trusted by
How does DataOps impact data governance?
Adopting DataOps principles in daily operations helps establish solid foundations for leveraging 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 a single source of truth and foundation for any data-related activities.
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 is 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.
Take the next step
Let’s treat the core problems, not symptoms.
Estimate my project
Receive a personalised project estimate and take the first step towards bringing your idea to life.
Discover success stories
Explore our case studies and find out how we have helped some of our clients from around the world.
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, optimisation, routine utilisation 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
What benefits can you get?
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.
Our solutions fit every need
For startups
With us, you can establish a reliable data backbone and implement data governance, data pipeline, and MLOps practices to set the stage for scalable success.
For small and medium-sized businesses
Streamline operations and improve decision-making to drive more value from your existing resources.
For enterprises
Turn your data into a strategic asset with the right data governance, data pipelines, and MLOps frameworks.
Discover why customers choose Altamira
CTO, SOLJETS
Ryan Crawford
CEO & Co-founder, Aquiline Drones
Barry Alexander
CTO, Ticker Tocker
Jonathan Kopnic
IT Solution Team Leader
Dusan Barus
CEO, CTRL Golf
Ian Cash
Head of Services, Universal Edition
People also asked
MLOps, short for Machine Learning Operations, is a derivative of DevOps but focuses specifically on the unique needs of machine learning lifecycle management. Unlike DevOps, which revolves around software development processes, MLOps emphasises the complexities of deploying, monitoring, and maintaining ML models in production environments. Contact us to learn more.
An MLOps pipeline refers to the sequence of processes involved in taking a machine learning model from development to production. This includes data preparation, model training, testing, deployment, and monitoring. Get in touch to learn how MLOps can benefit your business.
A machine learning data pipeline is a structured sequence of processes that systematically transform and transport data from its raw form into a format suitable for machine learning models, facilitating efficient data preprocessing, feature extraction, and model training.
AI data governance refers to the policies and practices that ensure high data quality, security, privacy, and ethical use of data in AI systems. High AI data quality is critically important as it directly impacts AI models’ accuracy, reliability, and fairness. Poor quality data can lead to erroneous outcomes and biased decisions. To learn more, contact us.
AI governance encompasses the strategies, policies, and regulations that guide the ethical, responsible, and effective use of AI technologies. Techniques include ethical AI frameworks, compliance with data protection laws, transparency in AI operations, and continuous monitoring for bias and fairness.
Yes, AI algorithms can be effectively used for data cleaning by identifying and rectifying errors, inconsistencies, and missing values in datasets. By implementing AI DataOps strategies, companies can streamline their data management processes, enhancing efficiency and accuracy in their data-driven decision-making. Contact us to learn more about data management.
AI excels at data analysis, particularly in handling large volumes of data, uncovering patterns, and making predictive analyses that are often beyond human capability. By incorporating AI data pipelines into a system, businesses can significantly improve the speed and accuracy of their data analysis.
All our services
Software Development
Holistic Software Care
Software Engineering
Expertise
Artificial Intelligence
AI Feasibility Studies
Looking forward to your message!
- Our experts will get back to you within 24h for free consultation.
- All information provided is kept confidential and under NDA.
