Machine learning has seen an explosion of development in the last few years and shows no signs of slowing. Forbes reports the global machine learning market was valued at $1.58B in 2017 and is expected to reach $20.83B in 2024, growing at a CAGR of 44.06% between 2017 and 2024.
Why? Executives have learned how to use AI to capitalize on the high-quality data already at their fingertips to generate solutions to complex problems, faster, more accurately and more scalable than a manually programmed solution.
Machine learning models learn, identify patterns, and make decisions with minimal intervention from humans leading to increased efficiency, lower costs, greater performance, and increased profitability.
Building a Machine Learning model that is meaningful to your business and provides an ROI takes a clear understanding of the possibilities, potential use cases as well as risks. Our team of architects and engineers will work with you to invest in the right solutions that can scale with your company’s growth and help you achieve your business objectives.
What We Do in Machine Learning
- What to Know about MLaaS
- Using Machine Learning and SageMaker with Tensorflow to Recognize Tulip Varieties
Machine Learning Assessments
The machine learning system assessment gives a high-level view of a current machine learning model(s), data collection and preparation processes, model training processes, deployment strategies, and monitoring systems. The assessment will cover characteristics surrounding security, reliability, scalability, model performance, system performance, and biases. It may also cover system modifications for cost savings, lowering risk, and improving operations.
What You Gain: An in-depth analysis of strengths and weaknesses along with an actionable list of recommended steps, best practices, and improvements with suggested priorities.
Machine Learning Software & Model Architecture
Software architecture for machine learning includes data collection, data preparation, training architecture, deployment architecture, inference architecture, and ongoing model analysis. This could include cloud deployment, edge deployment, or a hybrid strategy. Identifies the high-level machine learning design patterns, technologies, frameworks, testing methodologies, and DevOps methodologies that best fit the project use cases.
What you Gain: A comprehensive architecture for the full machine learning life-cycle from data collection and training to deployment and monitoring. Depending on SpinDance’s findings and recommendations, the architecture may include a block diagram of how all of the pieces of a full machine learning pipeline would fit together and/or a proof of concept initial pipeline.
Machine Learning Software Development
ML Model Development
Development focused on the actual creation of machine learning model artifacts. Primarily developed in Python and R. This service focuses on the design of experiments, data collection, data ground-truthing, iterative model training and tuning, and validation of model results. Frameworks used include Tensorflow, MxNet, and GluonCV.
ML Software Development (Cloud)
Development focused on hosting machine learning models in the cloud. Uses serverless infrastructure through platforms like AWS, Azure, and GCP. Includes creation of a data ingestion pipeline, database backend, hosting model code, deploying model updates, testing, and monitoring/alerting. Solutions are designed around security, scalability, performance, and reliability.
ML Software Development (Edge/Mobile/Web)
Development focused on hosting machine learning models on edge devices. Edge deployment could include embedded devices, mobile devices, and web applications. Includes creation of supporting code around the model to trigger data collection, pre-process data, run inference, and handle results. Solutions are optimized for speed, security, cost, memory size, and power consumption.
Machine Learning Maintenance & Development
ML Model Maintenance & Development
Model and software maintenance for existing machine learning models. This includes bug fixes, training based on new data, improving data collection processes, and refining model outputs, updating deployed models and adding new features.
ML Software Maintenance & Development (Cloud)
Software maintenance for existing cloud-based deployments of machine learning systems. Includes fixing bugs, updating code to maintain compatibility with software dependencies, deploying new models, and adding new features.
ML Software Maintenance & Development (Edge/Mobile/Web)
Software maintenance for existing edge-based deployments of machine learning systems. Includes fixing bugs, updating code to maintain compatibility with software dependencies, deploying new models, and adding new features.
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