AI & ML Technologies
Our vision encompasses an immediate future where companies base their operations and decision-making on data. It’s what we call “Data Products,” which are products that are supported in the cloud and essentially rely on AI techniques.
Sagemaker facilitates data science during generation, training and implementation of Machine Learning models. With the added value of operating models and deployment features, it provides a very complete work environment with added integrations and features.
Data preprocessing is often required for optimal functioning of ML models, to apply the correct format, filtering or transformations. This can be supported by services such as EMR, Batch or Glue, the latter having the additional benefit of being able to catalog data.
Multiple services in AWS provide data analysis, both in the exploratory phase and in other phases. Some examples are: Athena, for querying data in S3; Kinesis Analytics, for analyzing streaming data; and QuickSight, for displaying data.
The Rekognition service can recognize several types of objects in images and videos, especially facilitating facial recognition. Textract is used to extract data and text from scanned documents (OCR).
Polly can transform text into voice and Transcribe does the opposite. Translate can translate between languages; Comprehend is used for NLP (natural language processing); and we have Lex for chatbots.
AWS has other high-level AI services, such as: Personalize, for recommendations; Forecast, for prediction; and Fraud Detector, for detecting possible fraudulent activities online.
Machine Learning Success Cases
At Keepler we work to help companies generate value from data through Data Products. These products leverage the flexibility of the public cloud and machine learning techniques in all phases of a data project, with an MLOps approach based on automation and monitoring at every step towards a complete ML system: building, integration, testing, deployment and productive management.
This allows us to build more scalable, fast and smart solutions adapted to the needs of each company.
At Keepler we believe that technology is a means of creating value based on data, and that AI is the best technique for extracting it. Whether it is a proven solution or a search for new approaches, we will be with you every step of the way in building successful solutions using our experience and know-how.
Depending on the nature of the project, we may propose an approach based on CRISP-DM (Cross-Industry Standard Process for Data Mining). This methodology consists of a standard open process method backed by AWS in its Well-Architected Framework guide applied to Machine Learning (ML Lens). To supplement this framework, and for proper project management, we incorporate events focused on risk management, planning, review and continuous improvement through a work framework like Scrum. The combination of the two frameworks allows us to delivery iterative and incremental value throughout the Machine Learning project, in continuous collaboration with the customer.
Some use cases that rely on AI
Customer management is growing in complexity, with more demanding customers, more points of contact and more data. AI is the best way to gain a true understanding of how to improve interactions with our customers, choosing the correct channel and the right content. AI helps organizations improve user experience with information from their systems and other channels.
The number of products and services grows every day. At the same time, interactions through digital channels are increasing (e-commerce, digital platforms like Netflix, Spotify). It is important to have solutions that let you offer the right product or service at the right time, combined with business rules managed by the business users.
TIME SERIES AND DEMAND FORECASTING
AI can forecast upcoming sales, visits to websites, and electricity and fuel demand in a specific area. Time series forecasting gets information about the sources of the forecast, and identifies the events with the most impact and what-if scenarios.
NATURAL LANGUAGE UNDERSTANDING (NLU)
The NLU algorithms can manage an enormous volume of data generated in different documents. Some examples of these capabilities are: Detection of highlighted points in texts, classification of intention and taxonomy, and meaning analysis.
VOICE, TEXT AND IMAGE RECOGNITION
Advancements in AI bring new possibilities. Audio transcription and image and video recognition have many uses, such as detecting damage and recognizing individuals. The great number of algorithms and techniques offered by the different frameworks and services designed by cloud providers simplify and accelerate this process.
OPTIMIZED MODEL PRODUCTIZATION
It is possible to get model training and production deployment with a single click, including: Automated A/B tests, optimization of variables, automated training and monitoring of model precision.