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Can’t find any other pre-trained ML model for your requirements? We at Space-O develop custom ML models for this very purpose with both convolutional and recurrent neural networks (CNN & RNN) to give you highly adaptable ML solutions.
Space-O holds over a decade of experience developing computer vision solutions for security checks and autonomous visual inspections. Our certified ML developers utilize advanced image classification algorithms to amplify the value of visual data for faster analysis and decision-making.
Our NLP models help you automatically recognize user behavior, and gain in-depth statistics just based on raw customer data from comments or conversations. This can also be used to automate customer support processes by allowing the NLP model to decipher customer queries.
Space-O’s experienced professionals understand that an ML model requires progressive re-training and fine-tuning periodically to remain accurate. We offer fine-tuning services for both existing and brand-new machine learning solutions.
Capture new leads from existing users with our custom recommendation engines. These recommendations are strategically delivered by analyzing user behavior and preferences based on interaction data, performed autonomously by the ML model.
Whether it is a custom or existing ML solution, proper integration is necessary to prevent compatibility issues and performance bottlenecks. We at Space-O take all measures including data preprocessing, conversion, and infrastructure provisioning to ensure smooth integration.
Recognize images within a wide pool of data using a single sample with Space-O’s custom image recognition model development services. The inference models powering our image recognition models can detect any target from multiple media inputs. This can be used to enhance law enforcement, media copyright infringement, and general surveillance.
Space-O allows businesses to witness what their consumer base is looking for from their products and services by engineering advanced user intent deciphering models. These models analyze text from reviews, comments, and online interaction metrics to understand user sentiment. This leads to better brand resonance and upselling opportunities for any business.
ML-powered anomaly detection systems supercharge your quality assurance processes as they can be trained to recognize the nuanced anomalies within your products. Such solutions enable complete automation and amplify the identification of any unauthorized irregularities.
AI, predictive analytics, and machine learning help you make informed business decisions backed by real data and historical patterns, thus forming the foundation of successful business strategies.
Laborious manual processes cutting away at your time? Use a fine-tuned ML model to do the job for you. No matter which industry you serve, custom ML models can be tailor-fitted to your needs and automate any repetitive task in your way.
Detecting anomalies before they get out of hand can be vital to operations’ prevention of catastrophic damage. An ML model built to monitor operations does exactly this and helps safeguard against impending liabilities.
Leveraging ML technology’s analytical capabilities can greatly help determine customer behavior. This data can then deliver personalized recommendations to boost sales and customer engagement with your platform or service.
Whether you operate a physical production facility or are building a virtual product, ML models can be utilized to detect any anomalies during production, helping maintain a predefined quality standard across the board.
Give your legacy application a much-needed makeover by adding ML-based features to enrich overall functionality. Allow your users access to experience greater efficiency and fulfillment when using your application.
Know exactly how your operations are going at all times. Using ML models to power your operations translates into consistent output and proactive feedback. Combined, they make your operations easy to predict and manage.
Never miss shipments or inventory replenishment cycles with ML solutions that monitor all business operations. Even simple decision-tree ML models can help your business predict shortages or delivery bottlenecks ahead of time.
At Space-O, all of your project details are kept under strict confidentiality through pre-signed NDAs signed by all individuals involved and through a vigilant eye on all development activities.
A primary concern for businesses looking for ML solutions is the cost of maintaining the infrastructure and allocating dedicated funds for it. Space-O Technologies allows you to leverage serverless computing resources like AWS and GCP, and you only pay for what you use.
The team at Space-O holds over 14 years of experience developing custom machine learning solutions on non-neural and neural networks. Our 80+ pre-vetted developers also have experience using multiple machine learning libraries, including Keras, Caffe, and TensorFlow, so you get only the best, most experienced developers working on your project.
At Space-O Technologies, we believe in making the experience of developing an ML service as swift and painless as possible. This is why we ensure autonomy while conducting weekly follow-ups on the status and details of projects.
Space-O offers flexible engagement models to help you build your ML system conveniently. We have a model to fit every business need, whether a startup or an enterprise.
With this cost model, you can get a fixed contract value for your artificial intelligence software development project. Our ML engineers offer start-to-end support throughout your project’s lifecycle to ensure successful development.
Only pay for the resources you use for your ML development project using the time and material model. Developing ML solutions with this model helps you get a rough estimate to raise optimal funds.
Build your own team of ML developers and machine learning experts with the dedicated ML software development model. Hire pre-vetted ML engineers from Space-O through our sleek hiring process.
Space-O can augment your existing team and fill skill gaps. We have a team of onboarding-ready developers with on-site and off-site working flexibility for easy staff augmentation.
To better understand the prolific experience of our developers, check out the tech stack that our developers use to bring your ideas to life.
Backend
Machine Learning Libraries
Monitoring and Analytics
Healthcare
Education
eCommerce
Logistics and Travel
Agriculture
Manufacturing
Banking
Entertainment
A basic ML project costs anywhere between $20,000 – $25,000. However, the cost remains largely variable depending on the scope and complexity of your project. This variation in cost also applies to MVPs and POCs with differing complexity.
At Space-O, machine learning projects are usually built and deployed on cloud infrastructure facilitated by AWS. This includes services such as Amazon S3, EC2, Redshift, SageMaker, and AWS Deep Learning AMIs to give users a safe and well-known infrastructure provider to work with.
An entry-level ML project, like a basic image classification model, from ideation to deployment, takes anywhere between 2-4 months to move toward completion. Everything from model training to QA testing is performed within this timeline to ensure that the ML solution is fully ready for deployment.
Of course, a crucial part of ML development services is retraining and fine-tuning the ML model over its lifespan. Our ML fine-tuning services are ideal for this scenario as their sole objective is to improve the performance of existing ML solutions beyond what they were engineered for.
We try to keep our clients posted every week about the progress of their ML solution with industry-standard apps such as Jira, Slack, Confluence, and GitLab. However, we do comply with any communication requirements the client may have for us. This approach helps us maintain operational clarity while making the entire process seamless.
Yes. When you approach Space-O with your idea, we help you navigate the available solutions and determine which would be optimal for your use case. We have guided new brands and existing enterprises in choosing the perfect AI/ML solutions based on their precise needs.
Indeed, our solutions consider your existing systems during the development phase to ensure they are tailored to your requirements and workflows. This includes data structure, application interfaces, output mediums, and APIs. Such a proactive approach allows us to integrate brand-new solutions into any organization relatively easily.
Angular
React.js
Vue.js
Django
Flask
Express.js
TensorFlow
Pytorch
Keras
LangChain
Prometheus
Grafana
ELK Stack
MongoDB
Cassandra
Redis