What Is MLOps Consulting and Why Your Business Needs It

What Is MLOps Consulting and Why Your Business Needs It

Dorota Owczarek - November 21, 2022

If you’re like most business owners, you’re probably interested in artificial intelligence (AI) and machine learning (ML). After all, these technologies have the potential to help your business grow and become more efficient. But implementing AI or ML can be difficult, especially if you don’t have a lot of experience with them. That’s where MLOps consulting comes in. In this blog post, we’ll explain what MLOps is and why your business needs it. We’ll also discuss some of the benefits of using MLOps services. So keep reading to learn more!

MLOps vs. DevOps

Generally, DevOps is a set of practices and tools that helps organizations to manage the software development lifecycle better. The term “DevOps” was initially coined to describe practices emphasizing collaboration and communication between software developers and operations professionals. However, the term has since been adopted by a wide range of organizations, each of which has its own unique take on the concept. As a result, there is no one-size-fits-all definition of DevOps. However, some common themes often emerge in DevOps initiatives, such as a focus on automation, continuous delivery, and monitoring.

MLOps (Machine Learning Operations) is a relatively new term often used to describe approaches to managing and delivering machine learning models. MLOps encompasses model management (MLOps), data management (DataOps), and general development management (DevOps). Like DevOps, MLOps helps organizations to manage the machine learning development lifecycle better. MLOps goes one step further by also helping to automate machine learning workflows and supporting data engineering and management.

MLOps = ModelOps + DataOps + DevOps

MLOps = ModelOps + DataOps + DevOps

Like DevOps, MLOps initiatives typically involve automating repetitive tasks and incorporating feedback loops to help improve the quality of ML models over time. As a result, MLOps can help organizations speed up the delivery of machine learning applications while also reducing the risk of errors. What is more, MLOps also emphasizes the need for close collaboration between data scientists and machine learning engineers, as well as the importance of using software development best practices when building and deploying machine learning models. MLOps implementation - The process of model data preparation and model development with experiment tracking

MLOps implementation - The process of model data preparation and model development with experiment tracking

What Is MLOps Consulting, and What Does It Involve

MLOps is a set of practices combining software development and operations to improve machine learning projects’ speed, quality, and reliability. MLOps consulting services can help organizations to implement these practices to enhance their ML workflows. Machine learning lifecycle step-by-step

Machine learning lifecycle step-by-step

MLOps encompasses the support for the whole machine learning lifecycle. It covers everything from data preparation and preprocessing, feature engineering, model development, and model training to model deployment and monitoring. By automating these tasks, MLOps can help organizations to iterate faster on their machine learning models and get insights more quickly. Additionally, MLOps can also help to prevent errors and improve model performance.

Many different MLOps tools are available, depending on the organization’s needs. Some common MLOps tools include complete machine learning lifecycle management frameworks like MLFlow, modular data science code management tools like Kedro, orchestrating workflows or pipelines with tools like Apache Airflow, orchestration platforms like Kubeflow for Kubernetes steps, data management platforms like Datarobot, and model management platforms like Amazon SageMaker.

MLOps consulting services can help organizations implement these practices to improve their machine learning workflows. These services can help organizations choose the right tools for their needs, set up their infrastructure, enable model monitoring, enable continuous integration and delivery, and automate their machine learning processes.

The Different Types of Services That an MLOps Consultant Can Provide

An MLOps consultant can provide various services to help organizations implement and manage their machine learning initiatives. Typically, these services can be divided into four main categories: data management, model management, infrastructure management, and deployment management.

Data Management Services

Data management services help organizations to ingest effectively, store, manage, and govern their data. This includes automating data collection and data preparation processes, feature engineering, developing data pipelines, setting up data security controls, and establishing procedures for managing data quality. It also covers data sets versioning and training, and test data preparation.

Model Management Services

Model management services help data scientists build, train, and deploy machine learning models. This includes selecting the right ML models, optimizing model performance, and enabling iterative experimentation for model development. It covers tracking training datasets, model-building code, and model artifacts. Repeatable processes of continuous experimantation on the ML model

Repeatable processes of continuous experimantation on the ML model

Infrastructure Management

Infrastructure management services help organizations provision and manage the underlying infrastructure needed to support machine learning initiatives. This includes setting up cloud-based environments for development, staging, and production, configuring compute resources and managing networking and storage resources. Step-by-step development operations executed at development, staging, and production environments

Step-by-step development operations executed at development, staging, and production environments

Building Scalable ML Pipelines

A critical factor in achieving success with ML is building scalable pipelines for data processing and model training. A scalable pipeline provides automation for continuous integration, delivery, and deployment. For ML projects, you also need to set up training pipelines and a continuous learning pipeline that will enable model retraining in the production environment. By automating these tasks, you can improve your machine learning projects’ speed, quality, and reliability.

As some ML models require massive amounts of data to train, it is important to have a scalable pipeline in place for big data collection and preparation. A scalable pipeline can handle increasing amounts of data without overloading or breaking down. MLOps consulting services can help organizations set up such pipelines, allowing them to process large amounts of data efficiently.

Like any other software project, machine learning models also need version control. Versioning needs to be set for the model itself and also for the data sets. New data and updated data sets need to be properly tracked and traced with data versioning systems.

Model Deployment Management Services

Deployment management services help organizations to deploy a machine learning model in production. This includes managing model versioning, monitoring model performance, and managing model deployments.

Implementing continuous integration for machine learning projects and a setup for version control and deployment to dev, stage, and prod environments

Implementing continuous integration for machine learning projects and a setup for version control and deployment to dev, stage, and prod environments

Model Monitoring

One of the most critical aspects of MLOps is model monitoring. Monitoring helps organizations ensure that their machine learning models perform optimally and not degrading over time.

Organizations must constantly monitor their models’ performance and update them when needed. MLOps consulting services can help organizations set up automated model monitoring processes through a dedicated MLOps platform, enabling them to detect any model performance issues promptly.

MLOps consulting services for model monitoring can also help data scientists quickly identify and resolve model drift and data drift when the characteristics of the data used to train models change over time. This could lead to inaccurate predictions if not detected and addressed quickly.

An MLOps consultant can provide various services to help organizations implement and manage their machine learning initiatives. By working with an MLOps consultant, organizations and their software engineers can benefit from expert guidance in all aspects of machine learning operations.

ML Project Management

MLOps also cover a piece of project management practices that focus on communication and general organization. This includes setting up agile processes for managing collaboration between data scientists, engineers, and other stakeholders involved in an ML project. These processes help to ensure that all members are on the same page and are iteratively working towards the same goal.

If you’d like to learn more about the benefits of ML agile project management, we recommend reading our article on agile AI practices. It lays out how the idea becomes a complete product in an agile process and outlines the advantages and disadvantages of this popular methodology.

MLOps Consulting Process

MLOps consulting process typically involves the following steps:

Assessment

The first step is to assess the organization’s current state and identify areas where improvement is needed. This includes understanding the business goals, data resources, and compute infrastructure in use.

Planning

Once the assessment is complete, the consultant will work with the organization to develop a plan for implementing MLOps processes. This includes setting up data pipelines, configuring compute resources, and establishing model monitoring procedures.

Implementation

The next step is to implement the plan developed in Step 2. This includes setting up cloud-based environments, configuring compute resources, and managing networking and storage resources.

Training (in DevOps and MLOps)

For employees to be able to use the new MLOps processes effectively, they need training on how to use them. The consultant will provide training on how to build scalable pipelines for data processing and model training and deploy models in production.

Production Deployment

The consultant will provide guidance on deploying ML models in production, including model versioning and performance monitoring.

Monitoring

The consultant will also help organizations set up continuous model monitoring processes, allowing them to detect and address performance issues quickly.

Maintenance

The consultant will also provide ongoing support to the organization to maintain its MLOps processes. This includes troubleshooting any issues that may arise and ensuring that everything runs smoothly.

When to Reach Out for MLOps Consulting

With the increasing popularity of machine learning and artificial intelligence, many organizations are looking for ways to implement these technologies into their business. However, these technologies can be challenging to implement and manage effectively. While many companies have begun implementing MLOps internally, there is still a lot of confusion and frustration around getting it right. This is where MLOps consulting comes in. A good MLOps consultant will have a deep understanding of both machine learning and software development and will be able to help you build an efficient and effective pipeline for your business.

As a result, many organizations are turning to MLOps consulting services. MLOps consultants can help organizations to plan and implement AI technologies, as well as to optimize and troubleshoot their existing implementations. They can also provide guidance on best practices for MLOps, such as model management, data management, and orchestration. As a result, MLOps consulting can be a valuable resource for organizations looking to adopt or enhance their use of machine learning and AI.

As anyone who has implemented machine learning knows, many moving parts exist. Data scientists need to select and tune algorithms, engineers need to build and deploy models, and business stakeholders need to monitor results and track metrics. MLOps is a relatively new field that helps to address these challenges by providing tools and best practices for managing the end-to-end ML lifecycle.

If you’re struggling to keep your ML initiative on track, reach out for MLOps consulting. A good consultant will help you to streamline your process, identify inefficiencies, and implement best practices. As a result, you’ll be able to get your machine learning project up and running smoothly - and start seeing the results you’ve been hoping for.

Anytime you need help with machine learning projects and are unsure where to turn, it is worth considering MLOps consulting. Machine learning is a complex process that requires significant expertise and experience to execute effectively. If you do not have in-house ML talent or are not confident in your team’s ability to deliver results, then working with an external consultancy can be a wise investment. An experienced MLOps consultant can help you to plan and execute your project, providing valuable insights and guidance along the way.

ML consultants can also provide support during the delivery phase, ensuring that your project is delivered on time and within budget. In short, if you need assistance with any aspect of your machine learning project, then reaching out to an MLOps consultant can be smart.

The Benefits of MLOps Consulting for Businesses

Any business that wants to stay competitive in the modern world needs to be data-driven. Data helps companies make better decisions, understand customers, and optimize operations. However, collecting and analyzing data is only part of the equation. To really get the most out of data, businesses need to implement a robust MLOps strategy. By working with an MLOps consultant, companies can reap the following benefits:

Scalability

MLOps consultants can help businesses to scale their ML projects by providing guidance on best practices for model deployment. By setting up automated pipelines, businesses can deploy their models quickly and easily.

Improved Software Quality

MLOps helps to ensure that machine learning models are properly tested and validated before they are deployed to production. By enabling automated testing, MLOps consultants can help businesses identify any potential issues before they become a problem. This helps to avoid costly errors and improve the overall quality of the software.

Faster Time to Market

An MLOps consultant can help streamline the process of developing and deploying machine learning models. This can help businesses get new products and features to market quickly and efficiently.

Flexibility

MLOps consultants can help businesses to adjust quickly as market conditions change, allowing them to make the most out of their data and remain competitive.

Reduced Costs

Implementing an effective MLOps strategy can help businesses save costs by reducing the need for manual processes and increasing automation.

Additionally, outsourcing MLOps can be cheaper than running AI initiatives in-house. Hiring more experienced external consultants may look more expensive, but there is evidence to the contrary. Once you factor in all salaries, certifications, and training, the price tag gets hefty. These invisible costs explain the growing popularity of outsourcing, where prices are more predictable – and investors love predictable ROI in AI projects – instead of splurging on an in-house team that’s not always fully used, funds can be directed where they make an actual difference.

Implementing Machine Learning Models on Production? Invest in Machine Learning Operations Services

Development and operations teams have long struggled to work together effectively, leading to costly delays and suboptimal software releases. The emergence of DevOps attempted to address this problem by automating many of the tasks involved in software development and delivery. However, DevOps has its own challenges, particularly regarding machine learning. ML requires a different approach than traditional software development, one that is characterized by rapid iteration and constant experimentation. As a result, businesses are increasingly turning to MLOps consulting firms to help them implement an effective DevOps pipeline for their ML initiatives.

MLOps consultants bring a wealth of experience and expertise to the table, helping businesses avoid the pitfalls that can lead to failed ML projects. They can help companies design end-to-end MLOps pipelines that include everything from data collection and preparation to model training and continuous deployment to the production environment. In addition, they can guide how to best use open-source tools and technologies to support your ML initiative. By working with an MLOps consultant, businesses can empower data scientists and reap the benefits of the AI system while avoiding the challenges associated with traditional software development and machine learning lifecycle.

Looking for External Support in your Machine Learning Projects? Consider nexocode Team

If you are looking for ways to improve your business through data science and machine learning, MLOps is the service that will support your project. Investing in MLOps services can take your business to new heights and achieve success like never before.

With the help of an experienced ML consultant, you can learn everything there is to know about MLOps and implement it for the benefit of your company. Don’t wait any longer – contact us today to get started on your journey to success!

About the author

Dorota Owczarek

Dorota Owczarek

AI Product Lead & Design Thinking Facilitator

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With over ten years of professional experience in designing and developing software, Dorota is quick to recognize the best ways to serve users and stakeholders by shaping strategies and ensuring their execution by working closely with engineering and design teams.
She acts as a Product Leader, covering the ongoing AI agile development processes and operationalizing AI throughout the business.

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