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Navigating The Shift from Model Development to AI Production: A Guide for Data Scientists

Navigating The Shift from Model Development to AI Production: A Guide for Data Scientists | Wallaroo.AI blog

Part 2 in our Practitioners Guide Series, read Part 1 here or download the full ebook here.


AI has emerged as a transformative force for businesses, promising insights, automation, and competitive advantages. 

However, moving from developing models to getting them up and running in production is no walk in the park. The old-school method of crafting models in a bubble often crashes when hitting the complex reality of real-world applications. There is a gap between prototyping models in controlled environments to translating them into real-world applications. 

As a result, organizations invest substantial resources in data science talent and infrastructure, yet struggle to operationalize their models effectively. This disconnect not only wastes effort but also means many AI projects don’t actually add value where it counts.

According to Gartner, last year, only 4% of businesses had advanced their generative AI projects to the production stage. This year, that number has grown to 10%, showcasing a shift towards the broader adoption and operational deployment of AI technologies. This increase signals a more strategic and practical use of AI, moving beyond experimentation to realizing tangible business benefits and competitive differentiation.

Businesses Advancing Generative AI to Production Grown from 4% to 10% YoY (Gartner)

Making ML models perform outside the safety of a lab is a beast of its own. There’s a big leap from developing models to actually deploying them in production. Let’s dive into why this gap exists and how you can leap over it, turning your hard work into real-world results.

Step 1: Framing the Business Problem

Any AI project starts by understanding the business problem we are trying to solve, from clarifying stakeholders’ needs and determining the best approach to address them.

But let’s be honest, translating what the business wants isn’t always straightforward.

For example, when a business stakeholder requests a model for estimating the lifetime value (LTV) of customers, what do they actually mean? Are they after a dollar figure on each customer’s head, or are they scouting for VIP customers to shower with extra attention in their marketing efforts?

This distinction will shape everything from the ML models you choose to the benchmarks for success you aim for:

  • What’s our working definition of LTV?
  • How might different parts of the business apply LTV insights?
  • And how do these applications sway the selection of ML models and the goals we set for them?

Nailing down the exact nature of the business problem is your launchpad. After that’s in the bag, it’s onward to the next phase.

Step 2: Data Preparation and Cleaning

Sometimes it feels like data scientists are part-time detectives, part-time cleaners. A huge chunk of our time goes into cleaning, organizing, and preparing data.

This involves removing irrelevant or redundant data, handling missing values, dealing with outliers, and converting the data into a suitable format for analysis.

  • What data do we have available for model training?
  • What steps do we take to handle missing values, outliers, and biases in the data?
  • What tools and techniques will we use to organize and structure the data?

Once these questions are answered and the necessary preparations are made, we can move on to the next step.

Step 3: Model Selection, Training and Validation

Experimenting with multiple models, comparing their results and selecting the best-performing one is part and parcel of any AI project. This step also involves splitting the data into training, validation, and test sets to evaluate the model’s performance.

  • What criteria do we use to select an ML algorithm for our problem?
  • How do we fine-tune the chosen models for better performance?
  • What techniques do we use to avoid overfitting or underfitting our model?

This is the core of our expertise as data scientists – to select the most appropriate ML algorithms for the problem at hand and then fine-tune these models by adjusting various parameters and hyperparameters to achieve better performance. 

Once a model is trained, it’s time to  evaluate and validate it using metrics that measure its performance. This step helps identify any weaknesses of the model and make necessary improvements.

Step 4: Integration and Deployment

Now we’re stepping into a whole new arena: bringing our star model into the real operational framework of the organization. This is the grand finale, where we ensure that our model not only performs well but is also efficient, robust, and reliable in live environments.

However, this step, and the subsequent monitoring of the model’s performance steps,  fall outside the traditional expertise of many data science teams. Deploying our model into the production environment often ventures beyond what many data science teams are trained for.

As data scientists we excel at slicing and dicing data to reveal insights, but the nitty-gritty of keeping a model up and running in the wild? That’s a different beast.

ML models deployed in production must meet stringent performance, scalability, and reliability requirements. Yet, the data team’s expertise lies in analyzing and manipulating data, not necessarily deploying and maintaining models in a production setting. This is where cross-functional collaborations between data scientists, engineers, and IT professionals become crucial.

Unfortunately, this is where often things go wrong. 

  • Models that perform well in testing fail miserably when deployed into the real-world environment. This can happen due to various reasons like data drift, changes in user behavior, or inadequate monitoring and maintenance of the model.
  • Oftentimes, models are not properly integrated with the existing infrastructure or systems, leading to performance issues and inefficiencies.
  • Lack of communication and collaboration between different teams can also result in delays and difficulties in deploying the model into production.
  • Once deployed, ML models need to be continuously monitored for performance degradation, concept drift, and other issues.

Why Do We Struggle with Deploying Models to Production?

There are several reasons,  both technical and human, that can hinder the successful integration and deployment of machine learning models.

At the heart of the matter is a clash of worlds. Data scientists, with their deep dive approach to model building, often find themselves at a crossroads when it’s time to transition their creations into operational settings. The reason? A distinct difference in objectives and skill sets. Data scientists excel in the realms of algorithm development and data analysis, while the expertise required to navigate the intricacies of production environments typically falls within the domain of engineers and MLOps professionals.

ML deployment requires specialized expertise in both data science and engineering. Data scientists are skilled at building models, but they may lack the expertise to deploy them effectively in production environments. Similarly, engineers may lack the domain knowledge required to optimize ML models for business outcomes.

According to Gatner, while 45% of organizations have AI projects in pilot phase, only 10% currently have successfully deployed their models to production. This gap reflects the fundamental challenges that data scientists often face when trying to deploy their models.

As data scientists we are trained to explore and analyze vast amounts of data, develop statistical models, and derive insights from them.  During experimentation, we focus on exploring and understanding data, often using interactive environments like notebooks. Our approach to programming is exploratory rather than focused on structured, production-ready code.

This mindset can lead to well-performing models but also to code that’s difficult to deploy. In many cases, ML engineers take over to make the models ready for production. This transition process can be time-consuming and disrupts the workflow of ML engineers responsible for monitoring and maintaining the production environment.

Even after deployment, the data scientist’s role continues as we  may need to troubleshoot issues or monitor models for concept drift. However, we often struggle with this task as the production environment is an unfamiliar territory or we don’t even have direct access to the deployed models and pipelines.

Bridging the Gap with Production AI Platforms

We urgently need tools that let us train production-grade models, roll them out, and keep an eye on them in the wild, all while sticking to our preferred way of working. Nobody wants to drop their creative flow just to wrestle with deployment.

There is an urgent need for tools that enable data scientists to develop production-ready code, deploy models and monitor their performance in production without disrupting their interactive workflow.

AI Production Platforms such as Wallaroo.AI provide a bridge between model development and deploying models in production, allowing data scientists to focus on what we do best while also ensuring efficient collaboration with ML engineers. Imagine MLOps on tap, lightning-fast inference server, easy optimization and management in production for peak model performance, and fully integrated workflow. 

With self-service MLOps, blazing fast inference serving, continuous optimization, seamless integration, and easy collaboration, Wallaroo.AI empowers data-driven organizations to unlock the full potential of their data and drive business growth. By providing a unified platform for end-to-end AI operations and fostering collaboration between teams, Wallaroo.AI eliminates barriers to adopting AI and enables organizations to scale their AI initiatives.

Reach out if your team is looking to start getting models into production, or if you already have a few models in production and are looking to scale.

Read Part 1 the Practitioner Guide series here.



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