Having a unique mix of expertise in MLOps for FinTech, EdTech, real estate, retail, and monitoring services, our specialists ensure painless adoption of new models and ML infrastructure that save AI software versioning for your business and brings the whole production environment to the next level. We improve concept and data drift, preventing the degradation of ML models in data engineering, implement experiment tracking, and automate and simplify data preparation and model monitoring.

What Our MLOps Services Include

MLOps Consulting MLOps Consulting is all about scaling and avoiding potential risks while implementing changes that lead your business to growth:

• Analyzing the capacity of the existing solution, identification of weaknesses, audit of model performance, and recommendations on how to fix it.
• Choosing and adopting approaches for model training, scaling methods and problem-solving processes.

MLOps Development MLOps Development stage focuses on the implementation itself and carefully organizing the system:

• Optimizing AI solutions to address the needs of a particular system or client. Improving the solution by focusing on the client's requirements and business goals.
• Deployment of infrastructure under MLOps so that all parts of the system work as a smoothly managed orchestra.

Geniusee uses several AWS services to help organizations implement and manage machine learning models in production. Here are some examples of AWS services that may be used:

• Amazon SageMaker: Geniusee uses Amazon SageMaker to develop and train custom machine learning models, and then deploy those models to production with SageMaker endpoints. SageMaker can help automate the training and deployment process, enabling organizations to more quickly and easily deploy their models.
• AWS Lambda: Geniusee uses AWS Lambda to build serverless applications that can perform specific functions in response to events, such as invoking a machine learning model in response to a user request. This service can help organizations to reduce the operational overhead of running their machine learning models in production.
• [Amazon S3](Amazon S3): Geniusee uses Amazon S3 to store large amounts of data used for training and inference, as well as to store model artifacts. This service can help organizations to ensure data privacy and compliance while also providing easy access to data for model training and inference.
• AWS Glue: Geniusee uses AWS Glue to build and manage ETL (Extract, Transform, Load) workflows that prepare data for machine learning model training. This service can help organizations to automate the process of cleaning and preparing data for model training.
• AWS Step Functions: Geniusee uses AWS Step Functions to build and manage workflows that orchestrate the deployment and management of machine learning models. This service can help organizations to more easily manage the complexity of deploying and scaling machine learning models in production.

MLOps Across Industries

FinTech As FinTech is often about automation and supporting operations with sensitive data and processing tons of data simultaneously, MLOps is a brilliant service to implement in it. It allows you to train and integrate machine learning into your applications and use all the benefits of ML model innovations.

EdTech Working with big data, sales processes, and forecasting will become more precise, safe, and efficient with MLOps tools. Get the analytics on how well components of your system work from storage to the client and use it for your business decisions with model training.

Monitoring Services For this kind of project, MLOps allows collecting, analyzing, and defining insights from data in the media to serve your business goals.