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- Benefits for Organizations
- Implementation Stages: Gathering a Team of Professionals
- MLOps Tools
Reading time: ~4 minutes
More and more organizations are coming to understand the benefits of implementing machine learning, from the gains in profitability that AI helps realize to its usefulness in making decisions.
However, companies are often in a hurry to implement this technology and do not fully understand what difficulties may arise when introducing AI into business processes. Organizations may spend a huge amount of money on the development, while incorrectly perceiving the technology as just a new type of software.
In reality, AI is not just another technology to put on top of your existing stack. If its implementation is not planned carefully with input from specialists across the business, then you will not get the results you expect. All of this can lead to a huge waste of resources. The solution is to introduce machine learning operations or MLOps.
Let’s look at the steps involved in implementing MLOps, as well as what machine learning tools organizations can use to get the best results and solve their problems.
Today, as most companies are looking to find new ways to improve performance, machine learning can offer a pathway for creating reliable models. The scope of use of such operations is not limited to the IT sphere.
Here are the benefits for organizations:
- Speed of data collection and preparation. A huge number of data professionals spend excessive time on repetitive activities related to data collection and data preparation. Only after data is obtained can they begin to develop models and apply them in practice, an initiative that takes a lot of time and effort as well as a significant budget. To start developing a model, it is necessary to assemble a team of professionals from various fields of knowledge. This significantly speeds up the operations associated with the development of models, which leads to a reduction in costs.
- Ease of management and scalability. These operations have the advantage of rapid scalability. Specialists can manage a huge number of models and control and monitor them.
- Fast problem resolution. Specialists can use these complex tools for monitoring and visualization, as well as detecting any inaccuracies. The operations are based on high-precision analytics methods, which can detect any problem areas, and specialists immediately receive notifications about them, helping them to resolve and fix problems quickly.
Assemble various professional teamsfor successful implementation, including:
- Business specialists who identify problematic points;
- Data engineers who deal with all issues related to data collection, transformation, and loading;
- Data scientists who help process and analyze the data up to the model development stage so that it meets all the requirements;
- Software development specialists who help deploy models on various platforms;
- Machine learning architects who help improve models;
- Programmers who are engaged in system integration, as well as interface design development.
First, specialists should turn to various sources for both historical and online data. In addition, they take care of the process of storing data so that it can be quickly analyzed. Engineers and data scientists at this stage work together.
As they work on building models, experts extract data from sources to label them and identify some patterns. Then comes the moment of training, as the specialists begin to check the models. The teams are working to create ML pipelines that automatically collect and prepare all the necessary data, set characteristics, and train models. During the training of models, various parameters are used, as well as algorithms. Next, the model must be evaluated for testing.
In this step, the team operations and models to external services, which can be business applications. During this process, the main thing is to check that the machine learning application is running smoothly. If companies are not going to integrate ML components into applications, then the whole process can become extremely difficult. Ultimately, the goal is to provide security for the transition to the server that the company uses. It can be either a public cloud or a hybrid one.
At some point, services and applications based on artificial intelligence become integral factors. At this stage, specialists are engaged in adding data and codes, tracking any problematic points related to data quality, and also checking models for inaccuracies.
This is where architects come into play to oversee operations. Since the lifecycle of machine learning operations is not linear, any problems can lead to returning to the earlier stages of software development.
Once you decide to integrate MLOps, you will find that your organization has a huge number of opportunities to find solutions to problems. You should consider using either open-source tools, proprietary tools, SaaS, or on-premises solutions.
Any user should be able to access the source code without any problems, and they can also modify it for their purposes. In the case of proprietary tools, this is not possible, since users will not be able to easily access the source code easily. Only specialists in the software development organization have access to it, and only specialists can make any changes.
Choosing the first option, internal hosting, is used to gain access to local software solutions. The advantage of this solution is that organizations can provide a high level of security. However, this entails high costs for maintaining the infrastructure. By choosing the second solution, the programs can be accessed through software-as-a-service.
Implementing machine learning operations is a lengthy process that ultimately helps organizations increase productivity ands reduce costs. For implementation, it is necessary to involve professionals from various fields of knowledge to make sure that each stage is executed seamlessly.
Thank you for reading!