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Does your AI development agency provide MLOps services?

David Horvath's Picture

David Horvath

Budapest

A complete AI solution that delivers business value needs to be approached not just from a data standpoint, but from an ongoing operational perspective as well.

Artificial intelligence, or AI, is a term that has been tossed around a lot lately. It seems like every other business is using it in their marketing materials and trying to convince you that they need to use it too. But what is AI, really? And more importantly, how can it benefit your business? In this blog post, we will discuss the basics of AI and how businesses can use it to improve their operations. We will also explore the difference between MLOps services and traditional AI development, and why MLOps services are so important for businesses today.

AI is a process of programming computers to make decisions for themselves. This can be done in a number of ways, but the most common method is through machine learning. Machine learning is a process by which a computer “learns” from data, without being explicitly programmed to do so. This allows businesses to use AI to automate tasks that would otherwise need to be done by humans.

There are a number of benefits that businesses can enjoy by using AI. Firstly, it can help to improve efficiency and productivity. By automating tasks that would normally be done by humans, businesses can free up employees to work on more important tasks. Secondly, AI can help to improve decision-making. By analyzing data and making decisions for itself, AI can help businesses to make better decisions, faster. Finally, AI can help to improve customer service for example. Businesses can use AI to interact with customers to provide a more personalized and tailored experience.

Now that we’ve covered the basics of AI, let’s take a look at MLOps services. MLOps is a relatively new term that stands for “machine learning operations.” In short, MLOps services are a type of AI development that focuses on creating the infrastructure and processes needed to support machine learning. This includes tasks such as data collection, cleaning, and preparation, model training, deployment, and monitoring just to mention a few.

Traditional AI development agencies may not be able to provide MLOps services. This is because MLOps requires a different set of skills and knowledge than traditional AI development. As MLOps is a relatively new field, there are not many MLOps service providers out there. However, there are a few companies, like Lexunit, that specialize in MLOps services as well.

While there are many benefits to using AI, it’s important to understand the difference between MLOps services and traditional AI development. MLOps services are a new type of AI development that focuses on the creation of the infrastructure needed to support AI applications. This includes things like provisioning and managing data, training and deploying models, and monitoring and optimizing performance. Traditional AI development, on the other hand, focuses on the development of the actual AI applications themselves.

But let's see how MLOps services benefit your business

It is a collaborative function that consists of data scientists, DevOps engineers, etc. The ML model goes through the development, integration, testing, deployment, and monitoring steps as DevOps. This automates the deployment of machine learning and deep learning models in massive production systems, streamlining the management process. Aligning models with both business demands and regulatory standards is simpler.

  1. Increases productivity

    MLOps increases the productivity of data scientists and machine learning engineers. Some way in which it happens is by creating automated pipelines. There are many repetitive tasks in ML modeling. MLOps stand for automating the entire workflow of the ML model. This saves time and avoids human-induced errors. We can avoid wasting time on repetitive tasks and make value-added tasks by standardizing ML workflows for efficient collaboration. This reduces compatibility problems and quickens the construction and deployment of modeling processes.

    The increase in productivity is evident when implementing machine learning models and after implementing the models. The efficiency of model implementation has also improved over time as machine learning engineers become better at developing new models.
  2. Less Expenditure

    If we have one model, there is no need to hire extra people to develop new versions.

    Since there is no need to purchase additional hardware and software tools for delivering model versions, a significant portion of the operational costs can be avoided.

    It makes it possible for you to identify and minimize errors methodically. Reduced model management errors will also result in lower expenses.

    Automation reduces the need for manual management of machine learning models. Employee time will be freed up as a result and may be put to better use.
  3. Reproducibility

    Automating ML workflow provides reproducibility and repeatability regarding how the machine learning model is deployed. This helps in becoming more productive by reducing the time to deploy models.

    Coding automation also makes the job of data scientists easier. It enables the writing of code with little assistance from humans. Additionally, because ML keeps the portions of algorithms that work, it lets data scientists avoid repeating the time-consuming trial-and-error procedure, saving time and money.
  4. Reliability

    MLOps makes ML pipelines more reliable, human error will be less, and we can get real insights into the data.

    For reliable scaling, MLOps streamlines model management procedures.

    The machine learning model we get as a product of MLOPs is highly reliable with very few errors and high quality with high accuracy.
  5. Connectivity

    MLOps enable distributed teams of employees to collaborate on a common delivery model. It reduces deployment time and costs. Also, data collection and training of new models can be done independently by a department or team. Collaborating on a common distribution model allows all departments to share and process data more quickly and efficiently.

    A data scientist alone cannot work on achieving the goals of MLOps, so collaborating with others who work in IT and infrastructure development is essential.

    Building an integrated team ensures that these people with the skills necessary to make MLOps successful will work together to deliver the desired project outcomes.

    It allows seamless collaboration so that various teams can join hands in developing high-quality models.
  6. Monitorability

    Through MLOps, we get insights into model performance by

    - Retraining the model continuously to make sure that it gives the most accurate output.

    - Sending signals if there is any model drift (if the model degrades below a threshold).

    - This ensures that the process runs within regulations and that the program returns high-quality information.

Any company using ML technology would be wise to implement MLOps principles. As was mentioned above, MLOps and other tools like ModelOps can help you increase the efficiency of your company. This ensures that the ML solutions you use provide the value you want.

MLOps services are important for businesses because they provide the infrastructure needed to support AI applications. Without MLOps services, businesses would need to develop this infrastructure themselves, which can be time-consuming and expensive. Furthermore, MLOps services can help businesses to improve the performance of their AI applications. By monitoring and optimizing performance, MLOps services can help businesses to get the most out of their AI applications.

To run an AI application in the cloud, developing the AI model itself is not enough. The model has to be deployed on infrastructure and monitored for performance. This requires a different set of skills than traditional AI development.

Most big cloud providers like Google, Amazon, and Microsoft provide tools to support these MLOps activities requiring special skill sets to create MLOps pipelines. If you don't have these skillsets in-house then you will need to look for MLOps services from a specialized MLOps service provider like Lexunit.

Lexunit is an AI development agency that provides MLOps services. We can help you provision and manage data, train and deploy models, and monitor and optimize performance. Contact us today to learn more about how we can help you to improve your business with AI.