Overview of our Services
At Galific, we build futuristic custom machine learning models that exceed your business expectations. Regardless of your business, i.e., finance, healthcare, e-commerce, or manufacturing, it’s our immense pleasure to assist you in overcoming the biggest hurdles that you face in generating high ROI.
In addition, our tools also help detect fraud by building tailored models based on specific data patterns, behaviors, and risks of an organization. Consequently, our models ensure a personalized experience, tailored to your goals and the growth of your business.
What services do our custom machine learning models provide?
1. Domain-specific custom machine learning models
While we are studying your business workflow context, we will work closely with you to decide on the final model. So, our custom machine learning models would be great with your outcomes.
2. Custom machine learning models: Feature-rich Data
By normalization and extraction of valid features, our custom machine learning models effectively transform your raw datasets, making them impeccable in terms of accuracy and relevance.
3. Custom ML models: Training, Validation, and Hyperparameter tuning
After model building, we conduct rigorous tests and validation to ensure that our custom machine learning models exceed your expectations.
4. Continuous learning and custom machine learning models optimization
Despite post-deployment, learning and optimization processes are never-ending. We implement the user inputs via feedback loops, timely updates, and periodic retraining to improve our custom machine learning models.
5. Custom machine learning models: Well-explained and transparent
To ensure transparency and build confidence, our interpretable custom machine learning models give a valid explanation for non-predictions of some specific results.
Industries We Support
- 🛒 E-commerce – Personalization engines, dynamic pricing, customer segmentation
- 🏦 Finance – Risk assessment, credit scoring, fraud detection
- 🏭 Manufacturing – Predictive maintenance, quality control
- 🏥 Healthcare – Diagnostics assistance, treatment outcome prediction
- 🛍️ Retail – Inventory planning, behavior modeling, trend analysis
How do we help?
1. Gathering data to create machine learning solutions
Emphasizing that data is the most valuable asset for an organization, especially when it comes to creating custom machine learning solutions, we begin with the first phase: collect relevant data from authentic sources for training ML in making accurate predictions and giving you valuable insights. With the help of predictive intelligence, you can:
- Sneakpeak – To begin with, get a sneakpeak for detecting anomalies and trends in all departments.
- Determine – As a next step, determine the critical aspects of tracking metrics for performance enhancement.
- Prioritize – Also, prioritize smarter, adding predictive scores within your CRM or dashboards.
- Forecast – As a final step, forecast with confidence that anticipates demand, risk, revenue, and churn before it hits.
To begin with, we gather data to define your problem and understand your workflows, industry challenges, and growth metrics, which are essential for building a customized machine learning model.
2. Data preprocessing and cleansing – Custom Machine Learning Models
Accurate data is essential for a successful business. Hence, you would need impeccable data for building custom machine learning models for your workflow. At Galific, we perform data preprocessing and cleansing that includes converting raw data into an understandable format. Additionally, it proves to be valuable during training and testing phases, ensuring that our custom ML models exceed your requirements. In this phase, we focus on:
- Eliminating null, irrelevant, and inaccurate values.
- Data normalization and preprocessing
Following the above steps, we ensure that our models deliver higher deliverability, a blend of accuracy, productivity, and overall performance.
3. Creating custom machine learning solutions with custom options
Since custom machine learning solutions would differ from one organization to another, our team at Galific carefully evaluates each problem’s criticality. Based on the results, we select the right model and segregate it into different categories.
- Classification – For illustration purposes, classification models are beneficial for detecting fraud, loan approvals, and filtering emails.
- Regression – Concerning the predictive outcomes, regression is used for forecasting sales, predicting prices, and ROI prediction, which estimates the lifetime value of a customer.
- Clustering – When it comes to segmenting data points, clustering is an unsupervised learning method where data gets grouped without predefined labels.
- NLP -To enable human interaction, our custom machine learning solutions understand, interpret, and give accurate responses in human language. With this, we process a multitude of textual data – customer feedback, emails, support tickets, and legal documents, enabling you to identify improvement areas, rectify them, and make informed decisions.
- Time Series Forecasting – If you are a stock trader, time series forecasting helps in stock price and demand prediction in an unpredictable market.
- Spotting anomalies -To detect irregularities at an earlier stage, our models help find anomalies or unusual data patterns that could be too good to be true. Also, they help identify fraudulent equipment failures or cybersecurity threats.
4. Custom model training
After building a custom machine learning model, Galific Solutions leverages deep expertise and advanced technology to train our models effectively. The training includes data extraction and preparation necessary for recognition and making accurate predictions based on the input. Our training method comprises:
- Feeding the pre-processed data into the selected ML algorithm.
- Extraction of raw data.
The ML algorithm constantly adjusts the internal parameters to reduce the differences between predictions and actual target values. We create personalized models for your domain that wouldn’t fit anyone else.
5. Testing, Validation, and ROI Benchmarking of custom machine learning models
Our work doesn’t stop after creating and training custom machine learning models. We evaluate their performance through testing, validating, and ROI benchmarking before deployment. We evaluate them on various metrics, based on the type of tasks, such as classification and regression. The conventional evaluation metrics for classification are:
- Accuracy – Measures the proportion of correctly classified instances out of the total cases.
- Precision – Calculates the number of accurate and optimistic predictions out of the total predictions.
- Recall – Evaluates the proportions of accurate optimistic predictions out of the overall positive instances.
- F1-score – Refers to a harmonic mean of precision and recall and returns the report of model performance.
Our evaluation measures the model’s capability in distinguishing between classes and uses confusion metrics to give a performance summary of the classification model.
6. Post-deployment upgrades with our custom machine learning models
It’s a final stage where custom machine learning models fulfill all metrics after creating and training. Once the model is completed, we integrate it into a production environment of your organization – that’s the time you get real-time experience. Before deployment, we ensure that:
- The system handles high user loads.
- Smooth and hassle-free operations without crashes.
- Timely updates of the models.
Furthermore, we work on the improvement areas and help upgrade the models based on feedback. Our solutions include the prediction of new and unforeseen data into the deployed model to enable practical decision-making.
Frequently Asked Questions
Q1. How are custom machine learning models different from a pre-built AI tool or SaaS solution?
Answer:
The key difference between Galific’s custom machine learning models and pre-built AI tools or SaaS solutions is that the latter aim to be a one-size-fits-all solution, focusing more on general data optimization rather than specific data optimization. However, Galific builds well-trained custom machine language solutions, especially for your historical data, tuned for your KPIs to solve practical and unique problems. It means that higher accuracy and ROI have more control over the entire pipeline.
2. Can I build custom machine learning models without clean or labeled data?
Answer:
Yes, you can build custom machine learning models without clean or labeled data. Many businesses begin in that way. However, our team helps you to audit your present data, clean it, and handle missing values, including labeling if required. It could be manual or semi-supervised techniques. Furthermore, we build futuristic data pipelines to keep your upcoming data organized and model-ready.
3. How much time would it take to create and deploy custom machine learning models?
Answer:
The timeline to create and deploy custom machine learning models depends on your case and data readiness. It would take 4-12 weeks for a typical project.
- 1-2 weeks for Data discovery & cleaning
- 3-6 weeks for Model building & testing
- 7-10 weeks for Integration & deployment
- 11-12 weeks for Performance monitoring & feedback loop setup
We provide our customers with a roadmap and checkpoints throughout the journey.
4. How can I ensure that our custom machine learning models wouldn’t degrade or become obsolete over time?
Answer:
Good question! We rigorously monitor custom machine learning models via performance metrics like accuracy, recall, F1-score, etc. Furthermore, we detect data drift when there is a change in data distribution and offer scheduled retraining where your model gets re-fed with new data from time to time, so you remain updated with ever-changing business trends and customer behavior shifts.
5. Would non-technical stakeholders understand your custom machine learning models?
Answer:
Of course, yes! We give utmost precedence to explainability using techniques such as SHAP values, decision trees, and feature importance plots. Also, it would be easy for the non-technical stakeholders to understand how our custom machine learning models arrived at their prediction, required for trust, compliance, and decision-making.
