You have to admit, the phrase “we need AI” sounds cool during calls with investors. In reality, businesses need something entirely different. Predicting customer churn, detecting fraudulent transactions in time, setting up meaningful recommendations, or automating quality control, that’s what we’re really talking about here.

DSaaS is an outsourcing model where you aren’t sold abstract hours of work from a single exhausted specialist. Instead, you’re given a ready-to-use, turnkey solution.

A DSaaS provider can handle data preparation, exploratory analysis, feature engineering, model development and validation, deployment, integration, and ongoing monitoring. Depending on the project, the result is a model, a dashboard, an API, a data product, or a full-fledged system integrated into the company’s workflow.

That’s why data science as a service isn’t “hiring a data scientist for a few months”. It’s entrusting a specific part of the data science function, or the entire project, to an external team so it delivers real value.

DSaaS, DaaS, MLaaS, and other spooky, scary acronyms

The market loves to come up with new terms faster than we can keep up with them. Data science SAAS, DaaS, MLaaS, and “data science platform as a service” all serve different purposes, even though their names make it easy to assume they were all conceived during the same naming meeting.

If you want to understand the differences between the main approaches in more detail, see Data Science vs Machine Learning vs AI: What’s the Difference?

Depending on the task, a company may outsource data science services, entrusting an external team with a specific project or a broader range of data science functions. Let’s translate “we want artificial intelligence” into plain language: “We need to predict demand for the next 30 days with a margin of error no greater than 5% with automatic data collection methods.”

Data audit. We take a look at what you actually have. At this stage, it often turns out that the database stores everything except the information we need, and access permissions were set up back when the company was first founded. During the data collection and preparation phase, automatic data collection methods may also be used if the necessary data needs to be obtained from multiple external or internal sources.

We explore and look for patterns. We dig into the numbers, looking for seasonality, outliers, and hidden relationships.

We build models. We select the right algorithms, perform feature engineering, and train several variants.

We put the model through its paces (validation). What works beautifully on a test set may behave strangely in real life. We test the model under rigorous scenarios.

We deploy it to production. The finished model is connected to APIs, CRMs, ERPs, data warehouses, dashboards, edge devices, or other systems where its output is truly needed. 

We maintain it. Because data tends to change, the world doesn’t stand still, and models begin to drift over time. 

Who is responsible for what? A good DSaaS team includes a data scientist, a data engineer, an ML engineer, and a project manager. The data scientist is responsible for analysis, experiments, and models. The data engineer handles the data and the pipeline. The ML engineer deploys the development to production and is responsible for the relevant infrastructure. The project manager coordinates the technical and business aspects of the project.

That said, the expertise in your business always remains with you. There are no miracles here: outsiders will never understand your product better than you do.

The specific scope depends on the project, but data science services may include:

Organizing data pipelines and cleaning databases of junk data.

Building predictive models (scoring, churn, forecasts).

Setting up MLOps and monitoring to ensure the system doesn’t crash at the most critical moment.

Computer vision (image recognition software development services) and natural language processing (NLP), if you need to recognize documents or faces, or automate customer support.

In other words, data science professional services and data science and analytics services don’t end with training a model. The dividing line here is defined by the business objective, not by the name of the algorithm.

What’s great about it:

Speed: No need to spend half a year hiring hard-to-find experts, the team is already assembled and ready to dive into the project.

Synergy between roles: You don’t need to search for a data engineer and an ML specialist separately, they’re already working as a team.

Focus on results: The project is taken all the way to production, rather than ending with a nice-looking PDF report.

Where the problems begin:

Dependency risk: If the contractor has built a “black box” system and no one inside the company understands how it works, parting ways with them will be extremely difficult.

Security: Data transfer and compliance require special attention, especially in regulated industries. Access controls, storage, anonymization, retention, and user rights should be defined before working with sensitive data.

There is also a risk of losing in-house expertise. If all decisions consistently remain with an external team, the company ends up with a functioning system but lacks the people capable of working with it independently.

The quality of the results also depends on the data and the chosen approach. When evaluating such projects, it’s important to consider limitations related to bias in machine learning models, the quality of the source data, and the accuracy of metrics.

Finally, there’s the classic question: the model is built. What’s next?

For a more detailed analysis of typical data science problems, see Common Data Science Problems.

Production models require monitoring, infrastructure, retraining policies, an owner within the company, and a clear support process.

How much does it cost, and what determines the price?

There’s no one-size-fits-all price, and it would be foolish to promise one. It all depends on the pricing model:

Subscription: consistent monthly costs for a fixed amount of resources.

Time & materials: payment based on actual hours worked (ideal for research projects where the scope is still fluid).

Dedicated team: a full team of experts assigned to your project for the long term.

Fixed price: a fixed price for a clear and agreed-upon outcome.

Factors affecting the estimate:

Data maturity. Clean and accessible data differs significantly in cost from data scattered across multiple legacy systems. Data collection, preprocessing, and integration sometimes account for a significant portion of the project.

Model complexity. Simple forecasting and a computer vision production system with real-time inference require completely different resources.

Compliance and infrastructure. Working with regulated data, on-premises deployment, custom integrations, security controls, and continuous monitoring increase the scope.

You can read more details in Data Science UA’s “Understanding the Cost of AI Development: A Guide”. 

Retail. Data science is used for demand forecasting, customer behavior analysis, recommendations, merchandising, and supply chain management. You can read more about the application of ML retail. My team once developed a recommendation engine that increased the average cart size by 0.4 items and reduced churn from 20% to 15%.

FinTech. Common tasks here include fraud detection, risk modeling, churn prediction, transaction analysis, and financial forecasting. For more details on the use of AI and data science in the financial sector, see the article “AI in FinTech.” We have already developed predictive models for assessing customer churn and ML models for profiling and clustering transactions.

Manufacturing. Predictive maintenance, quality control, computer vision, and sensor data analysis are well-suited for DSaaS. For more details on the application of AI in manufacturing, see the article “AI in Manufacturing”. 

Pharmaceuticals. Data science can be used for forecasting, quality control, R&D, and supply chain optimization. In one of Data Science UA’s projects, ARIMA and LSTM models were used to forecast material consumption and sales, resulting in a reported 10–15% reduction in waste.

Energy. Here, models process production, weather, SCADA, and operational data. 

Logistics. Forecasting, route optimization, inventory analytics, and computer vision are used for various supply chain tasks. We once developed a low-latency OCR solution for our clients to recognize container numbers, which reduced the time spent on supply chain operations by a record 70%.

How to choose the right contractor

If you choose a company based on “who has the nicest website”,  you’re likely to be disappointed. Ask the key questions right away:

“Have you solved similar problems in our industry?”

“What happens after the model is deployed?”

“How is quality monitoring organized?”

Our experts will honestly tell you where your data isn’t suitable for ML. Our goal is to help your business achieve exactly the results you’re aiming for. After all, your legitimate expectations and our results go hand in hand.

If you need to implement a complex computer vision project with custom data annotation, it will take a little longer than a classic predictive analysis based on clean tables. For companies considering outsourcing data science, these kinds of questions are more useful than comparing twenty pages of identical lists of technologies.

Our goal is to ensure that the first 90 days result in concrete deliverables, rather than three months of meetings about just how promising AI might be.

And I (not only as the CEO and Founder of the AI service company Data Science UA) would be happy to tell you EVERYTHING about it 🙂