Machine learning services

At Yalantis, our commitment to delivering comprehensive machine learning services spans every stage of development, from building robust data pipelines to packaging models for deployment. Our ML development approach revolves around three main stages: the data pipeline, model pipeline, and deployment pipeline.

Machine learning as a service at the core of Yalantis’ value proposition

Yalantis has established end-to-end MLOps processes for efficient ML project execution. We opt for MLaaS, as it’s an end-to-end and documented approach to delivering accurate machine learning models within tight deadlines. With the ML as a service approach, we follow strict guidelines on how to train and integrate ML models but also manage to have enough flexibility during ML project execution to meet clients’ changing needs.

Building a data pipeline to ensure an ML model receives only high-quality data

The data pipeline is the core of any machine learning project, and the quality of an ML model depends on the quality of data used for its training, testing, and validation. Thus, building a data pipeline is the most time-consuming stage of the ML project. As an experienced machine learning service provider, Yalantis has expertise in setting up high-functioning data pipelines tailored to each business’s unique requirements. Building a data pipeline involves the following stages:

Data collection or ingestion. Yalantis data engineers are experienced in in-depth data exploration to gather data from diverse sources, including internal databases, data warehouses, data lakes, APIs, files, IoT devices, and real-time streams. This ensures a comprehensive and large enough dataset for model training. To provide accurate and fast results, reduce possible bias, and solve complex business-specific issues, ML models need to be trained on large amounts of data.

Data wrangling (cleaning). Rigorous data cleaning involves removing data inconsistencies, errors, outliers, and missing values from the data as well as ensuring data quality and reliability. This process can also include reformatting or restructuring data attributes to make datasets more suitable for feeding an ML model. As a machine learning services company, Yalantis has a tried-and-true (and thoroughly documented) data wrangling approach to make this process quick and efficient and reduce the time for ML model development.

Data splitting. Datasets are also collected and separated into three critical groups: training data (80%), testing data, and validation data. Data splitting is essential for deployment of an accurate and high-performance machine learning model.

Creating a model pipeline with continuous optimization and fine-tuning

Setting up a model pipeline revolves around developing and fine-tuning machine learning models to meet the project’s objectives with precision and accuracy. It’s the most important machine learning service, which defines how an ML model will perform and what results it will produce. Here are the steps this service involves:

Machine learning algorithm selection and experimentation. Our data scientists and engineers conduct extensive experiments with various algorithms and architectures (for example, microservices and event-driven) to identify the most effective solutions. We measure and compare different algorithms’ performance to select the top three to five options for model training.

ML model training. By combining machine learning algorithms with collected and effectively split datasets, we can proceed to model training. This process is iterative and requires constant evaluation of every step, considering how the model reacts to different data types. At this stage, ML engineers also conduct error analysis to effectively solve errors before deploying the model to production.

Hyperparameter tuning and feature engineering. As part of our extensive machine learning development services, we fine-tune model parameters to optimize their performance and ensure accuracy. We apply such techniques as cross-validation to make sure models perform as intended.

Model testing. At this stage, our QA specialists perform a range of all-around performance and functional testing as well as final model acceptance testing to make sure that the model is quick, accurate, ethical, secure, and high-functioning.

ML model deployment pipeline and model serving patterns

The deployment pipeline or model serving process transitions the trained machine learning model into a production environment where it can deliver real-world value. Deploying an ML model to production consists of the following steps:

Containerization and orchestration. Using Docker and Kubernetes as essential tools in our ML services, Yalantis MLOps specialists deploy models in distributed environments, ensuring efficiency and scalability. Containerization with the help of Docker involves wrapping the model, its dependencies, and its runtime environment into a standardized container. Kubernetes, an open-source container orchestration platform, enables automated deployment, scaling, and operation of these containers.

Testing and validation. Thorough testing and validation processes simulate real-world scenarios and are necessary to identify and mitigate potential issues before ML model deployment. As one of the end-to-end machine learning development services offered by Yalantis, testing ensures that the deployed model performs as expected under different conditions, including variations in input data and workload. The validation process verifies the model’s accuracy, reliability, and performance, ensuring its ability to provide meaningful predictions or insights in the production environment.

Monitoring and maintenance. Another MLOps process is continuous monitoring to ensure model performance remains satisfactory over time. ML model monitoring also involves tracking key performance metrics, such as prediction accuracy, latency, throughput, and resource utilization, to detect bias, deviations, or anomalies that may signal degradation in model performance.

Common ML model serving patterns

Model as a service. This pattern involves wrapping an ML model as an independent service that can be requested via a REST API in any application. In such a manner, applications can seamlessly interact with the model, sending input data and receiving predictions or insights in return. This decoupled or microservices architecture enables flexibility and interoperability, allowing diverse applications to use the same model for predictive analytics and decision-making tasks.

Model as a dependency. This machine learning service presumes directly integrating an ML model into an application or system as a dependency. The model becomes an integral part of the application’s functionality, providing predictions or insights when called upon within the application’s logic. Such an integration facilitates seamless interaction between the application and the model, enabling real-time decision-making and responsiveness.

Model on demand. This pattern involves dynamically loading the ML model when needed based on specific triggers or events within an application or system. Therefore, it is based on an event-driven architecture (machine learning business use cases). Instead of the model constantly running, it is executed only when a request for predictions or analysis is received. This approach optimizes resource utilization, leading to efficient use of computational resources and reduced operating costs.