In a recent conversation with the Data Science Group Mentoring community, I was struck by the growing prominence of the MLOps Engineer role. While the responsibilities of Data Scientists and Machine Learning Engineers are somewhat well-defined, the MLOps Engineer position seemed shrouded in a bit of mystery. Intrigued by this emerging role, I decided to delve into the world of MLOps, exploring both its theoretical underpinnings and real-world applications.
A radar plot of MLOPS skills
Decoding MLOps Engineer Job Postings: Unveiling Key Competencies and In-Demand Skills
To begin my investigation, I analyzed a sample of LinkedIn job postings for “MLOps Engineer” positions. Using a large language model, I mapped the skills required in these postings to the traditional set of MLOps competencies. This analysis yielded valuable insights into the skills and expertise sought after by employers in this field.
Essential tasks undertaken by an MLOps Engineer, as effectively summarized by Neptune.ai :
- Checking deployment pipelines for machine learning models.
- Review Code changes and pull requests from the data science team.
- Triggers CI/CD pipelines after code approvals.
- Monitors pipelines and ensures all tests pass and model artifacts are generated/stored correctly.
- Deploys updated models to prod after pipeline completion.
- Works closely with the software engineering and DevOps team to ensure smooth integration.
- Containerize models using Docker and deploy on cloud platforms (like AWS/GCP/Azure).
- Set up monitoring tools to track various metrics like response time, error rates, and resource utilization.
- Establish alerts and notifications to quickly detect anomalies or deviations from expected behavior.
- Analyze monitoring data, log, files, and system metrics.
- Collaborate with the data science team to develop updated pipelines to cover any faults.
- Documenting and troubleshoots, changes, and optimization.
Interviews with MLOps Engineers
Bridging the Gap Between Job Postings and Real-world Experiences
Next, I sought the perspectives of experienced MLOps Engineers through a series of interviews. These conversations provided me with a firsthand account of their day-to-day responsibilities, challenges, and rewards. The insights gained from these interactions complemented the data gathered from the job postings, painting a comprehensive picture of the MLOps Engineer role.
Here are the top valuable insights I got from interviewing MLOps Engineers on LinkedIn:
MLOps engineers specialize in operationalizing machine learning applications, managing CI/CD, ML platforms, and infrastructure for efficient model deployment, while Machine Learning Engineers (MLEs) may engage in MLOps tasks, especially in smaller teams, focusing on productionizing proofs of concept and utilizing CI/CD for deployments. In larger teams, dedicated MLOps roles emerge to handle the evolving complexities of scaling machine learning systems.
MLOps Engineers focus on crafting efficient infrastructure for model training and deployment, while ML Engineers concentrate on model building and fine-tuning. Collaborating in pipelines, both roles deploy models from data scientists to staging and production, monitoring the entire process. Despite different names, these roles are often considered synonymous, encompassing the same responsibilities in seamless model deployment and production monitoring.
MLOps Engineers are pivotal in transitioning machine learning models from concept to deployment, working in tandem with Data Scientists. Their responsibilities include storing ML models, containerizing code, crafting CI pipelines, deploying inference services, and ensuring scalability with infrastructure tools like Kubernetes and Kubeflow. Additionally, they monitor real-time inference endpoints to maintain continuous performance, and provide more accessible and reliable machine learning models for widespread use. MLOps Engineers thus provide a crucial complement to Data Scientists, enabling them to focus on their core expertise in ML model creation and ensuring that these models are not only innovative but also practically deployable.
MLOps Engineers bridge the gap between ML Engineers working in the lab and the production environment. While ML Engineers focus on creating machine learning models, MLOps Engineers intervene in the production phase, serving, monitoring, and ensuring the availability of models 24/7. MLOps Engineers often handle the entire lifecycle of a machine learning project, from data retrieval to model creation and industrialization in production. They are well-versed in technologies like Kubernetes, Docker, and CI/CD environments, distinguishing them from ML Engineers who primarily work with notebooks and scripts. MLOps Engineers are more familiar with open-source tools and platforms and play a crucial role in building ML platforms on top of open-source foundations.
MLOps engineers bridge the gap between data scientists and software engineers by overseeing the deployment, scaling, and maintenance of machine learning models in production environments, encompassing a wide spectrum of models, including large language models (LLMs) and computer vision models (CVOps). They ensure smooth integration with the overall system, monitor model performance, and handle the real-world application of those models. As LLMOps and CVOps become increasingly sophisticated, the role of MLOps engineers will continue to evolve to meet the challenges of managing these complex models.
The MLOps engineer plays a crucial role in the end-to-end lifecycle of machine learning (ML) models. They specialize in deploying, managing, and automating the entire model lifecycle. Their expertise lies in tools like Kubernetes, Triton, Infrastructure as Code (IaC), and more. In contrast to the data scientist, who focuses on model development , and the ML engineer in the middle, handling both development and deployment, the MLOps engineer is dedicated to the operational aspects of ML systems. Transitioning from a data scientist to MLOps often involves passing through the ML engineer role, and the specific roles required can vary depending on the company and use case.
The MLOps Engineer role is akin to the DevOps relationship, bridging the gap between development and operations. MLOps Engineer can also intersect with Data Engineer, and the extent of this overlap depends on factors such as organizational maturity and technical debt.
An MLOps engineer bridges the gap between ML development and production operations, focusing on deploying, managing, and optimizing ML models. They design and implement ML pipelines, maintain infrastructure, monitor model performance, and collaborate with data scientists and engineers. When models underperform, MLOps engineers collaborate with developers to retrain and improve them. They also ensure scalable deployment across company products and platforms, collaborating with network and IT engineers.
Advice for ML mature companies
Factors to consider when determining when to hire the first MLOps Engineer
Lev Udaltsov, an experienced ML Engineer who transitioned into an MLOps Engineer role, offers valuable insights into the factors that organizations should consider when deciding whether to upskill or hire MLOps engineers. His firsthand experience highlights the critical role that MLOps plays in overcoming infrastructure challenges, bridging skill gaps, and achieving long-term ML success.
Infrastructure Complexity: The Missing Piece
Lev’s experience working with both ML Engineers and DevOps professionals underscores the need for a dedicated MLOps role to bridge the gap between these two domains. He emphasizes that while both ML Engineers and DevOps professionals possess valuable expertise, the lack of an MLOps engineer can hinder progress. MLOps engineers bring a unique blend of data science and engineering skills, enabling them to effectively translate research-oriented models into production-ready applications.
Team Size and Skillset: Fostering Expertise
Lev concurs that team size and skillset play a significant role in determining the need for MLOps engineers. He points out that traditional data science teams often lack the operationalization and automation expertise that are essential for successfully deploying and maintaining ML systems. MLOps engineers fill this critical gap, ensuring that ML models are not only developed but also integrated seamlessly into the organization’s infrastructure and processes.
Long-term ML Strategy: A Vision-driven Approach
Lev’s observation that the need for an MLOps engineer depends on the organization’s long-term ML strategy is well-founded. He acknowledges that for organizations heavily focused on research-oriented ML initiatives, the immediate need for an MLOps engineer may not be as apparent. However, as organizations transition towards more production-driven ML applications, having an MLOps engineer becomes increasingly important to ensure the successful deployment and continuous improvement of these systems.
Additional considerations:
Cost-effectiveness: Upskilling existing ML engineers may be more cost-effective in the short term, but hiring experienced MLOps engineers can bring immediate expertise and accelerate your organization’s ML journey.
Time-to-market: If you need to deploy ML models quickly, hiring experienced MLOps engineers can expedite the process.
Retention: Upskilling existing employees can foster loyalty and engagement, while hiring experienced MLOps engineers can bring fresh perspectives and expertise.
Ultimately, the decision to upskill or hire depends on your organization’s specific needs, budget, and timeline. Carefully evaluate these factors to determine the most effective approach to address your ML requirements.
Companies need to have an MLops infrastructure that is aligned with the speed and quality of their ML products.
I'm not sure where this meme came from, but I'm glad it exists
Companies need to ensure they have the skills and expertise they need to manage and sustain a large ML portfolio.
Unknown meme lord strikes again!
Becoming an MLOps Engineer
A Roadmap for Aspiring MLOPS Professionals
In the ever-evolving landscape of data science, MLOps Engineers have emerged as the new rockstars, bridging the gap between the creativity of data scientists and the operational expertise of DevOps teams. Their mastery of automation, infrastructure management, and machine learning empowers them to ensure the seamless deployment, monitoring, and optimization of ML models, ensuring that the promise of data science translates into tangible business impact.
MLOps Engineers are not just technical wizards; they are also the glue that holds together the ML and DevOps teams, fostering collaboration and ensuring that ML models are integrated seamlessly into the organization’s operations. Their ability to communicate effectively with both technical and non-technical stakeholders makes them invaluable assets in ensuring that ML initiatives are aligned with business goals and that the benefits of ML are widely understood and appreciated.
From Data Science Foundations to MLOps Expertise
A Dialogue on Fostering Collaboration and Minimizing Overlap in MLOps Teams
As organizations increasingly embrace the power of machine learning, MLOps Engineers will continue to play an increasingly crucial role in ensuring the success of ML initiatives. Their expertise, communication skills, and dedication to bridging the gap between data science and operations make them the unsung heroes of the data science revolution, truly deserving of the title of “New Rockstars.“
Here is a dialogue to better reduce overlap of Machine Learning Operations roles:
If you are fascinated by the power of data and intrigued by the challenges and opportunities of bridging the gap between data science and operations, then MLOps may be the perfect field for you. With the right training and dedication, you can become a sought-after MLOps Engineer, helping organizations harness the full potential of machine learning to drive innovation and achieve their business goals.
Gratitude is extended to the dedicated MLOps Engineers whose valuable contributions significantly enriched this article:
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This is a personal blog. My opinion on what I share with you is that “All models are wrong, but some are useful”. Improve the accuracy of any model I present and make it useful!
