Skip to main content

Assembling MLOps practice - part 1

[PLACEHOLDER]
Image

In one of our previous articles it was highlighted how DevOps manages the End-to-End application cycle, leveraging agility and automation. CI/CD pipelines, collaboration and transparency, monitoring and automation are part of the list on how DevOps leverages and facilitates agility. What if then we bring those to support ML? That is how MLOps comes to the table and starts making sense!

Lego-Alike-data-assembly
Lego Alike data assembly - Generated with Gemini

A big tech corporation, or a startup, nowadays will see how it is becoming a requirement to incorporate AI and Machine learning (ML) in their operations. ML components are key parts of the ecosystem, supporting the solutions provided to clients. As a result, DevOps and MLOps have become part of the "secret sauce" for success. 

What is MLOps

Just to bring the definition of what you probably know (or put together based on the above) MLOps focuses on the life-cycle management of machine learning models. It combines machine learning with traditional data engineering and DevOps practices for building, deploying, and maintaining ML models.
MLOps makes sure that models are:

  • Reliable.
  • Scalable.
  • Efficient.

To accomplish this, a pipeline is established which includes tasks such as:

  1. Data collection.
  2. Model training.
  3. Deployment of the models.
  4. Monitoring of the models.
  5. Updates and enhancements.
MLOps-cycle
MLOps

How do you get started for setting an MLOps Practice?

You need:

  1. A framework.
  2. A team (multi-disciplinary) with the know-how.
  3. Technology.

DevOps-development-operations

Recommended article

SRE, DevOps and ITOps. If you are wondering what the differences between the SRE and DevOps are, as well as how these roles work with ITOps within an organisation then...


A framework:

  • Establish Clear Goals. The need of defining the business problems (use cases) that ML is meant to solve for your company. You need to be able to measure success, therefore you need to also define the OKRs and KPIs.
  • A culture of collaboration. In discussions about DevOps, MLOps, and agility, collaboration is the “glue” that serves as an integral component that binds these concepts together.
  • Ensure model governance and security. Proper audits to mitigate bias in the data, data processing consistency, and to ensure security (sensitive vs confidential vs PII vs public and accessible).
  • Model development pipeline. Having the right toolkit to set the proper automate data processing. CI/CD (Continuous Integration and Continuous Deployment) will keep the rhythm of the pipeline, which includes the unceasing iterative and improvement of the models’ refinement and its data. 
MLOps-building-blocks
MLOps building blocks and enablement

Who do you need?

Building a diverse, multidisciplinary team is crucial for the success of AI and ML projects, as it ensures the right combination of skills to achieve business objectives effectively. In MLOps, various roles play a key part in shaping and maintaining AI practices, though the specific requirements and team size may influence their composition. While these roles are not exhaustive, assembling a team with complementary expertise fosters innovation and enhances operational efficiency, ultimately driving successful AI initiatives that will improve the business outcomes and will contribute to the effectiveness and collaboration of the different business units and teams within the organization, such as data scientists, software engineers, and IT personnel, ensuring that ML models are continuously improved and updated.

Here are some of those roles that you would be needing:

  • AI Architects. They are responsible for designing the system’s architecture and ensuring that it is scalable and efficient.
  • Data Scientists. They are responsible for the models and algorithms.
  • ML Engineers. They are responsible for developing and deploying machine learning models and algorithms.
  • Data Engineers. They are responsible for designing, building, and maintaining the data infrastructure.
  • Software Engineers. They act as the bridge between machine learning models and production systems, ensuring smooth integration and long-term reliability. They do so by  developing and maintaining the software components.
  • Product Designers. They lead the UI and UX.
  • Domain Experts. They provide domain-specific knowledge and expertise in order for the crew mentioned above to come together, take the requirements and design the solution.
  • AI Ethics and SMEs. This part of the team are the ones making sure the AI system has the guardrails in place, keeping it ethical and socially responsible.

Difference between MLOps and AIOps

AIOps and MLOps are two distinct yet complementary paradigms that both leverage Artificial Intelligence (AI) and Machine Learning (ML). 

MLOps is a set of practices and framework designed to streamline and automate the lifecycle of machine learning models; making sure they run smoothly, they are scalable, and reliably in production environments. 

While AIOps, on the other hand, leverages AI-driven insights to provide solutions at a faster pace. AIOps, focuses on applying AI/ML technologies to enhance and automate IT operations, performing tasks such as anomaly detection, event correlation, predictive analytics, risk management, automated root cause analysis, etc.

Trending posts

AI Agents is the new thing to talk about

Tech is evolving faster than ever in this AI era, that it feels every week there is something new to talk about, and what you learn weeks back is no longer relevant, or “that AI tools” already has gone through changes that you need to catch up with in order to stay relevant.  Fear not, embrace the challenges and learnings, and find applications for it that are good and ethical for this present, and the hereafter.  The new “craze” is AI agents, and for good reason!  Image generated with NightCafe In contrast with AI chatbots, an AI agent can execute tasks on your behalf. If you are thinking “ that this could be agents that we leave running independently for many days for a group of deliveries ”… Well then you are correct! Are there risks? Should we talk about trust and accountability? The answer for both is yes. I already hinted at it a couple of paragraphs above, when I wrote “ good and ethical ”. AI (Artificial Intelligence) agents are software that work autonomously,...
Read more

SLA-SLO-SLI and DevOps metrics

Companies are in need of the metrics that will allow them to stay in business by making sure they meet the expectations of their customers. The name of the game is higher customer satisfaction by winning their trust and loyalty. To do so, you want to provide good products and services. Therefore you need to find ways to monitor performance, drive continuous improvements and deliver the quality expected by the consumer in this highly competitive market. Photos from AlphaTradeZone via Pexel and Spacejoy via Unsplash SLAs, SLOs and SLIs are a good way to achieve the above. They allow clients and vendors to be on the same page when it comes to expected system performance. If we go one level deeper, vendors/providers work on NFRs (Non-Functional Requirements) when working on their solutions. NFRs define the quality attributes of a system. I bring them up because the relationship between them and the SLAs is that they provide, in a way, foundational aspects for the SLA-SLO-SL...
Read more

The value of listening

 Have you ever been in a conversation that you feel at best is just a waste of time; at worst, that the other person does not care? Have you been the messenger in some of them, the recipient in another? We’ve all been there; at times with family, with friends, at work. When at work, has this ever happened to you in a group meeting, at a one-on-one; when giving or receiving feedback?   Photo by Christina Morillo via Pexels   I knew the type of manager I wanted to be from the start of my mentorship. As I continued to navigate the waters, it became increasingly clear that the path I had chosen was appropriate, and therefore, the type of future colleagues I wanted to be surrounded by, and the type of mentors I wanted to continue seeking guidance from.  For me, the path has always been of being a leader over a manager. I believe I can attribute that, first and foremost, to my family’s values and  principles, and the way my parents raised us. Secondly, my path in the ...
Read more

SRE, DevOps and ITOps

 If you are wondering what the differences between the SRE and DevOps are, as well as how these roles work with ITOps within an organisation then you are not alone; and best of all you are on the right blog post. Often enough business units in a company get confused, assigning the ServiceNow or Jira tickets or any other ticketing system of your preference, to the wrong group, and even having the incorrect expectations when doing resourcing. Let us go through definitions, insights and scenarios that will help you understand the difference. DevOps software development operations - AI Generated When it comes to DevOps and SRE, then you might be wondering which practice came first. While SRE may have originated a bit earlier, internally at Google, DevOps came first publicly as a practice and started to be used by companies. A few years later was when Google decided to open SRE to the world after the publication of the "Site Reliability Engineering" book. Therefore, technically sp...
Read more

This blog uses cookies to improve your browsing experience. Simple analytics might be in place for pageviews purposes. They are harmless and never personally identify you.

Agreed