In recent years, we have not stopped hearing the concept of DevOps and, in general, the word “Ops” refers to different areas. Although DevOps Certification in Hong Kong began around 2000, concepts such as DataOps came much later and started to be heard everywhere just a year ago. Between the one and the other, we find other terms such as MLOps, models, or AIOps. It may not yet be clear where these concepts come from, what they relate to, and whether they are actually effective. Is this a simple fashion or trend, or does it really make sense for companies?
DataOps Drivers
The deployment of automation of data-related tasks and data-related developments is becoming more critical and differentiated for companies. There are many challenges in data management today. The following are:
-Difficult to find profiles that perform all the tasks required to process data.
-The responsiveness required by the market in providing solutions.
-Consumers are becoming more demanding in terms of costs.
-A technology ecosystem with a dizzying evolution, in which new and better solutions are offered every day.
All of this creates the perfect mix that makes us think about how to work, how to “perform operations”, how to make robotic solutions, and how to provide the value that defines us as the best option.
From DevOps to DataOps
Operations automation was introduced with DevOps, but the truth is that even before that, operationalization had already been introduced into data management. Management of operations related to software has always been present, but now there is a need to speed up the process in order to achieve a cool result as soon as possible. For this reason, the management process is more focused and developed. The best place to get help is data science services.
If we think about creating DevOps, we see how he tried to combine two seemingly very different worlds: development and systems management. And, despite all the complexity, it was possible to combine both scenarios, creating a marriage that is already difficult to divorce. To date, no development team is considering working without shared code or an automated deployment tool, which makes it obvious to combine DevOps as a working methodology.
So, seeing the success and benefits that DevOps has brought, and with a constant vision for improvement in all aspects of data, the question naturally arises why not also implement the rest of the tasks that are performed with data. And this is where new concepts come in such as MLOps, AIOps, and, more recently, DataOps, which collects all this data-related operationalization.
So what is DataOps? How is it different from DevOps?
While DevOps is focused on technology, DataOps, as the name suggests, focuses on data and the value it derives from, beyond the technology layer on which it is supported.
Thus, we could define DataOps as a methodological framework that collects all the operations performed on data, from the moment it is captured to processing, and therefore provides a final view of its content. Here, the challenge for DataOps is to optimize the delivery and availability of information in order to do so as quickly and with the best quality as possible. All of this will finally allow you to contribute to the decision-making for the future and, therefore, will have a strategic impact on the company.
Different processes, different models. MLOps, ModelOps, and AIOps
In addition to the aforementioned DataOps processes focused on automatic information processing, we find other concepts that are also very relevant and will be very important for companies in the future:
MLS: This concept refers to how machine learning models work with a focus on bridging the gap between data scientists and IT operations teams. Thus, we are talking about automating the entire life cycle, which includes the introduction of a machine learning model into production: automatic software deployment, process monitoring, resource management, model configuration, model testing, evaluation of results, and error management.
models: Refers to a large set of maps processes that enable all AI models to be deployed and produced on a large scale. In other words, it covers the life cycle of all operations, both machine learning, and artificial intelligence.
AIOps: As we might guess, in this case, the model refers to artificial intelligence processes as well as advanced analytics used in IT areas. This allows them to move forward in identifying problems in the areas of technology infrastructures. Although this concept is somewhat further from the previous ones, since it does not directly refer to data, it is also very important to know it so as not to be confused with other terms.
Looking Ahead: What Are The Biggest Challenges At DataOps?
As we said, DataOps is a recent concept that was born in response to the needs of the environment, and therefore, in order to achieve the best solutions, some problems need to be addressed, such as:
– Data collection of the highest quality, both from a technical and functional point of view.
-Corrections using the best possible solutions and in an easy way.
-How to exchange data with third parties and use data from these third parties.
-Achieving greater efficiency in treatment.
-Forming operations in such a way that people are engaged in them without the need to have high knowledge or mathematical and/or statistical ability.
-Finding a symbiosis between people who process data to obtain value, the tools with which they are processed, and the value of the data.
DataOps operators must gradually respond to these challenges and must deliver real value to organizations by becoming not a trend anymore, but consolidated models that give rise to a new way of working and understanding data, delivering greater value every time.
