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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 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?

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?

Machine Learning operations / MLOps

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.

  • Remain adaptable, swiftly aligning data strategies with market demands and regulatory changes
  • Establish data pipelines to facilitate the integration and analysis of diverse data sources, needed for startups aiming to use data-driven insights
  • MLOps help enhance credibility and attract investors looking for maturity in data handling capabilities

  • For small and medium-sized businesses

    Streamline operations and improve decision-making to drive more value from your existing resources.

  • Implement data governance to simplify compliance with evolving data privacy laws, reducing the need for extensive legal consultations
  • Automate data operations to allocate human resources to more strategic tasks rather than routine data management
  • Enhance product offerings and customer satisfaction through more personalized and effective services

  • For enterprises

    Turn your data into a strategic asset with the right data governance, data pipelines, and MLOps frameworks.

  • Take advantage of data governance to enforce standard data definitions and processes across various departments, leading to consistent and reliable data insights
  • Ensure scalability and flexibility in handling data spikes and diversification, important for large-scale operations
  • Facilitate the adoption and integration of new technologies and algorithms across the business

  • Discover why customers choose Altamira

    Altamira produced superlative deliverables that provide valuable information to guide internal operations and support sales processes. The team was receptive to feedback, adapting resources to ensure effective collaboration.


    Ryan Crawford


    Custom-made ERP solution that provides jet brokerage services to boost jet sales and service quality.

    Services we provided

    • Web Application
    • UI/UX Design
    Communication was excellent and my original idea became real thanks to the brilliant work of Business Analysts and Project Managers. Their prompts were always on time, hence there were no misunderstandings in the process. I am absolutely satisfied with how my app looks and functions. It is exactly what I wanted to get when I decided to go with this team.

    CEO & Co-founder, Aquiline Drones

    Barry Alexander


    Android and iOS native applications that provide on-demand drone services, where users can connect with couriers and track the status of their drone order delivery.

    Services we provided

    • Mobile Application Development
    • UX/UI Design
    At first, I felt hesitant about trusting such a complex project to outsource developers. But, fortunately, my concerns appeared to be absolutely unfounded. Altamira team did amazing job! And I was pleasantly surprised by how well-established processes they have.

    CTO, Ticker Tocker

    Jonathan Kopnic


    Web, iOS, and Android trading platform that offers advanced capabilities in earning by trading, selling products via the integrated marketplace, and conducting trading live-streaming.

    Services we provided

    • Discovery
    • Tech Vendor Audit
    • Web and Mobile Application Development
    The team’s communication practices made for a rapid yet stable exchange of information, allowing for the quick resolution of all issues that arose during development.

    IT Solution Team Leader

    Dusan Barus


    Unique mobile solution that automates the process of uploading, transferring, documenting, numbering, and downloading pictures.

    Services we provided

    • Web Application Development
    • UX/UI Design
    It has been an absolute pleasure working with the team at Altamira. We have never been blocked or impeded by their work. They operate efficiently and quickly to get the job done.

    CEO, CTRL Golf

    Ian Cash


    Unique mobile application that aims to teach users to play golf according to individual playing styles and recommendations provided by specifically developed algorithms.

    Services we provided

    • Discovery
    • Mobile Application Development
    • UX/UI Design
    Altamira helped us to shape the future of music by launching our disruptive publishing tool scodo. What I appreciate most in working with them is their incredibly structured approach while keeping a strong focus on reaching our project’s goals. And of course all of that paired with highly talented and likeable people across the board.

    Head of Services, Universal Edition

    Entertainment & Media

    Electronic document delivery ecosystem development for music publishing.

    Services we provided

    • Web application development
    • Software Development

    People also asked

    How is MLOps different from DevOps?

    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.

    What is an MLOps pipeline?

    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.

    What is a pipeline in ML code?

    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.

    What is data governance in AI?

    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.

    What is AI governance and its techniques?

    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.

    Can AI be used to clean data?

    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.

    Is AI good at data analysis?

    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.

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