ARTIFICIAL INTELLIGENCE &
MACHINE LEARNING SERVICES

At Sense7AI, we offer end-to-end AI & ML solution services that deliver tailored solutions for businesses. Our team of expert data scientists combines creativity with strategic planning to deliver AI & ML solutions optimized for maximum impact and success.

Our rigorous process includes the following steps

Machine Learning Services

Data cleaning

We clean and preprocess the data to ensure it is high quality and suitable for analysis. We use various tools and techniques to clean and preprocess the data, including Pandas, Numpy, and OpenRefine. These tools help us clean, manipulate, and transform data to ensure that it is high quality and suitable for analysis. Our data cleaning process is important in our rigorous AI & ML solution development process.

Model Selection

We use a data-driven approach to select the most suitable model for each AI & ML project. The model selection process is crucial to ensure the final solution is accurate, reliable, and scalable. We consider several factors when selecting a model, including the nature of the data, the client’s specific requirements, and the available resources.
First, we analyze the nature of the data to determine the appropriate model type. For example, we use convolutional neural networks (CNNs) for image recognition projects, while for text analysis projects, we use recurrent neural networks (RNNs) or transformers. We also consider the size and complexity of the data, as well as any existing patterns or trends.
Next, we consider the client’s specific requirements, such as the level of accuracy needed, the available budget, and the timeline for the project. We work closely with clients to understand their goals and objectives and ensure that the selected model meets their expectations.
Finally, we consider the available resources, including hardware and software. We choose the appropriate tools and platforms to ensure that the selected model is scalable and can handle large volumes of data.
Once we have selected the most suitable model, we move on to the training phase. During training, we fine-tune the model to optimize its performance and ensure it meets the client’s specific requirements. We use various techniques, including supervised and unsupervised learning, to train the model and ensure its accuracy and reliability.
Machine Learning Services
training, software build, outsourcing developers

Training

We use various techniques to train our AI & ML models, including supervised and unsupervised learning.
Supervised learning is a technique used to train models with labeled data. In supervised learning, the model is trained on a dataset with inputs (also called features) and corresponding outputs (also called labels). The goal of supervised learning is to learn a mapping between the inputs and outputs that can be used to predict the output for new, unseen inputs. Some examples of supervised learning algorithms include regression, decision trees, and neural networks.
On the other hand, unsupervised learning is a technique used to train models on unlabeled data. In unsupervised learning, the model is trained on a dataset with inputs only, and the goal is to discover patterns and structure in the data. Unsupervised learning is often used for clustering, anomaly detection, and dimensionality reduction tasks. Some examples of unsupervised learning algorithms include k-means clustering, principal component analysis (PCA), and autoencoders.
In addition to supervised and unsupervised learning, we also use other techniques for model training, such as reinforcement learning, transfer learning, and ensemble methods. Reinforcement learning is a technique used to train models to make decisions based on feedback from the environment. Transfer learning is a technique to transfer knowledge learned from one task to another, while ensemble methods combine multiple models to improve performance.
During the model training process, we fine-tune the model to optimize its performance and ensure it meets the client’s specific requirements. We use various techniques and tools to monitor the model’s performance, such as cross-validation and confusion matrices.

In conclusion, at Sense7AI, we use various techniques, including supervised and unsupervised learning, to train our ML and AI models. By selecting the appropriate technique for each project and fine-tuning the model during training, we can deliver accurate, reliable, and scalable ML and AI solutions that meet our clients’ specific requirements.

Testing

We thoroughly test our AI & ML models to ensure they are accurate and reliable. Testing is a critical part of our development process and helps us identify any issues or errors in the model. We use a range of testing techniques to evaluate the model’s performance. Some of these techniques include:

Cross-validation is a technique used to evaluate the model's performance on a subset of the data that was not used during training. This technique helps us avoid overfitting, when the model performs well on the training data but poorly on new, unseen data.

A confusion matrix is a table used to evaluate the performance of a classification model. It compares the predicted output of the model to the actual output and shows how many true positives, true negatives, false positives, and false negatives the model produced.

A ROC (receiver operating characteristic) curve is a graph used to evaluate the performance of a classification model. It shows the true positive rate against the false positive rate for different thresholds of the model.

Precision and recall are two metrics used to evaluate the performance of a classification model. Precision measures the proportion of true positives among the predicted positives, while recall measures the proportion of true positives among the actual positives.

A/B testing is a technique used to evaluate the model's performance against a baseline or another model. It involves randomly assigning users to different versions of the model and measuring the performance of each version.

By thoroughly testing our models, we can identify any issues or errors and ensure that the model is accurate and reliable. We also fine-tune the model based on the testing results to improve its performance.

testing
In conclusion, at Sense7AI, we thoroughly test our ML and AI models to ensure they are accurate and reliable. We use a range of testing techniques, including cross-validation, confusion matrices, ROC curves, precision and recall, and A/B testing, to evaluate the model’s performance. By identifying and addressing any issues or errors, we can deliver ML and AI solutions that meet our clients’ specific requirements and deliver maximum impact and success.
Machine Learning Services

Deployment and Support:

We deploy the AI & ML models we develop in the client’s infrastructure to ensure they are readily available. The deployment process is an essential step in our development process, ensuring that the models are scalable and can handle large volumes of data.
Before deployment, we evaluate the client’s infrastructure before deployment to determine the most suitable deployment options. We consider the client’s data storage capabilities, security requirements, and available computing resources.
Once we have determined the best deployment option, we work with the client to install and configure the necessary software and hardware components. We ensure that the deployment is scalable and can handle the expected volume of data.
We also conduct thorough testing of the deployment to ensure it works correctly and meets the client’s specific requirements. We monitor the performance of the deployment and make any necessary adjustments to optimize its performance.

Once the deployment is complete, we provide ongoing support and maintenance to ensure that the model continues to deliver maximum impact and success. We work closely with the client to address any issues or errors that arise and provide regular updates and transparent communication.

We use cutting-edge tools and platforms for
innovation and efficiency, including

python1
Aws
tensorflow
pytorch
These tools help us develop AI & ML solutions that are accurate, scalable, and reliable.

We offer various services, including

Our natural language processing solutions use AI to analyze text and extract valuable insights. We use NLP tools, including NLTK, Spacy, and Gensim, to preprocess and analyze text. These tools help us extract meaningful insights from unstructured data and enable us to develop accurate sentiment analysis, chatbots, and language translation solutions.

Cross-validation is a technique used to evaluate the model's performance on a subset of the data that was not used during training. This technique helps us avoid overfitting, when the model performs well on the training data but poorly on new, unseen data.

Our image recognition solutions use deep learning algorithms to recognize and classify images

While our anomaly detection solutions use statistical models to identify anomalies and anomalies in data.

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To summarize, at Sense7AI, we offer comprehensive AI & ML solution services that deliver tailored solutions for businesses. Our expert team follows a rigorous process that includes data cleaning, model selection, training, testing, and deployment, using cutting-edge tools and platforms for innovation and efficiency. We offer various services, including predictive analytics, natural language processing, image recognition, and anomaly detection.

Contact us today to learn more about our AI & ML solution services and how we can help your business grow.