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MLOps Services

Machine Learning Operations (MLOps) are an important and often underestimated part of developing machine learning systems. They are tools and technologies that manage and deploy machine learning models throughout their lifecycles. Find out why it’s essential to keep it in mind when working on machine learning projects.

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MLOps Is Something You Should Do from the Start of Your Project

There are several reasons why skipping MLOps when working on a machine learning project can backfire badly. We don’t want this to happen to you, so here are several core benefits of doing it parallel to your deep learning project as an integral part of ML development.

1. You’re Investing in Steady and Reliable Development of Your Product

The initial cost of adding MLOps to your machine learning system for model development may seem redundant. However, it will help you avoid the abovementioned issues and warrant steadier development. If you implement the MLOps steadily from the beginning, your machine learning project can develop smoothly. It doesn’t mean you must use many tools from the start, but gradually add them as the needs evolve. It prevents dismantling the project if you decide to add MLOps later.

2. You Can Move from PoC to a Project  and Beyond More Seamlessly

Continuous monitoring and improving model performance allows for more straightforward progression and scaling between the project stages. It also makes it more manageable to adjust the models in production environments to different conditions, in case you’d like to apply these machine learning models in slightly varied settings or scales for new locations.

3. You Have Access to Experiment Tracking to Understand How the Model Development Worked in Time

Experiment tracking is an integral part of MLOPs that aids in developing machine learning algorithms and models. It allows you, data scientists and machine learning engineers to track the model drift changes and look into its evolution. It enables insights into previous stages of development to understand the evolution and address potential issues that come up later.

4. You Can Control Data and Model Versions and Improve Accordingly

Version control is another vital aspect of MLOps. You can have several versions of a model that can be trained on different datasets, and MLOps allows you to easily control these versions, allowing you to backtrack when necessary. Similarly, the data you use for training the machine learning algorithm can have different annotations for each version. Machine learning operations enable tracking data to each version and comparing the results to determine the set that performs best and understand why.

5. You Can Manage and Oversee Your ML Projects with Higher Transparency

Utilising MLOps brings you more transparency and a better understanding of your machine-learning projects, as many tools offer helpful dashboards. It allows constant progress monitoring and will enable you to move back and forth between what happened in each stage (PoC, MVP, etc.). It also makes it easier to catch potential errors as soon as they occur.

MLOPs Explained. MLOPs Services and Solutions

What Can You Gain from Us as an MLOps Services Provider?

Your Project and Its Infrastructure Will Be Fully Understood

With all-rounded experts in AI, ML and MLOps, you can count on us to understand the goals of your project and its architecture. It will create a solid basis for building mutual trust and understanding.

Your Datasets Will Be Thoroughly Prepared

Data is the key element in developing machine learning models. Our experts will help you evaluate the data you’ve gathered or give you valuable insights on how to collect it best and annotate it so that it’s meaningful for model training.

You Will Get Support for Model Deployment, and After

With MLOps, you can count on us to help you move smoothly between the development stages and seamless deployment. We will aid you in moving your models to production and offer you post-deployment support for an agreed period to ensure everything works as intended.

Your Project Will Be Constantly Monitored and Improved

MLOps helps assure continuous learning, enabling our experts to catch errors and mishaps before proceeding. It helps secure your MVP since it won’t suffer from mistakes resulting from the PoC stage, as they will be eliminated before proceeding. In turn, it results in smoother and faster product deployment. Standardised processes and best practices ensure that models are developed and deployed consistently with optimal quality.

You Will Get an MLOps Tool Landscape that Matches Your Needs and Preferences

We know everything about MLOps, so you don’t have to. If you have some favourite tools or cloud infrastructure you’re already comfortable with, we’ll adjust to it. So many tools are available to build a landscape that aligns with your architectural preferences. Many tools can be integrated. That’s why we ensure to pick those individually, depending on your specific use case. Please tell us what you need, and we’ll discuss the optimal MLOps services and approach.

MLOPs Implementation Process

Send us a word, and let’s work together on integrating MLOps in your project.

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You Don’t Need to Know Plan Yourself: Count on Our MLOps Services to Choose the Optimal MLOps Tools and Workflow

With our MLOps services, you don’t need to worry about anything beforehand. Our MLOPs experts will help you choose the right tools and methodologies to work with. Here’s what we offer:

A thorough discovery process

We will meet with you to discuss your needs or the machine learning project, which will help us understand your goals.

Model development

You will find assistance in feature engineering, learning and finetuning, and experiment tracking.

Model deployment

Once it’s time, you can count on us to prepare automated pipelines, handle model versioning and rollback, scale to cloud or edge environments, and orchestrate deployment.

Tool integration

Our experts stay current with recent technologies, frameworks, and tools. They’ll integrate your preferred choices or advise you on the best options from the vast landscape.

Model monitoring and management

Your deployed models will be continuously monitored and updated to scale with your growing needs.

MLOPs Tech Stack

Continuous Machine Learning: A Practical Example

Like DevOps, which has continuous delivery and integration, MLOps has continuous machine learning. It allows for monitoring model performance and catching errors or drawbacks in the model training process. An example will make this easier to explain.

Imagine you are working on a machine-learning model that detects people waiting to be seated in a restaurant. You have a chain of restaurants, but initially, you decide to implement it only in one. So, the data scientists prepare the data, and a team of ML experts use a supervised learning system to train the model.

After the model was deployed to the first restaurant, the model worked perfectly. You decide to deploy it to other restaurants in your chain. However, this model won’t perform the same in different restaurants, as it’s only been trained in that first setting. Even a slight deviation in lighting or furniture arrangement may influence its performance.

Continuous machine learning allows us to constantly monitor the model’s performance in different settings and find areas for improvement. This ongoing observation and reinforcement learning allows easier version adjustments and additional training for scenarios specific to the restaurant by adding new learning data or changing the model’s architecture. Automated machine learning tools enable it to be done much more efficiently than manual adjustments.

Continuous Machine Learning Example. Expert MLOPs Services

What Do You Risk by Neglecting MLOps?

Exponentially growing technological debt

If not appropriately managed, ML models in production quickly become outdated. Suppose the model isn’t continuously improved and updated with relevant data. In that case, it will become less relevant, resulting in subpar performance and ever-growing technological debt that is hard to catch up on and often requires rebuilding models from scratch.

You can’t keep track of the changes

Moving between project development stages without MLOps can be significantly more complicated since you can’t apply experiment tracking. Without it, you can’t backtrack to what happened on the PoC level. It’s especially crucial when the team lineup changes and new members need to know what happened in the earlier stages. Without MLOps, it might be impossible.

Project delays

When your in-house or outsourced machine learning teams can’t access the history of the ML models and often need to redo them, which results in delays and project schedule disruptions. In case of changes in the team, without MLOps, the knowledge gets easily lost and lacks continuity, which makes it harder for new staff to continue work.

MLOPs Services and Solutions

Use Our MLOps Services and Expertise to Build and Deploy Your Next Machine Learning Project

Now that you know how important it is to engage in machine learning operations from the beginning of the project, you can trust our experts to implement the best practices for your model development. And with artificial intelligence and machine learning experts on board, you can create end-to-end AI and ML projects. Don’t hesitate to reach out. Your future with automation awaits.

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Radosław Szmit
Radosław Szmit Chief Business Officer

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