We help companies of all sizes to drive value from Machine Learning. Our services offering is designed to accommodate different levels of Machine Learning adoption in the company. Our standard engagement types in the Data Science domain include:
ML Roadmap Design
We assist you on the road to embrace ML and AI in your business processes. We work together with your team to identify key improvement areas that can benefit from the use of advanced analytics within your company. We define the infrastructure and competencies needed to achieve the goal and create a game plan. The created roadmap incorporates industry best practices and inputs from your business. In our approach we are always tool-agnostic and first try to maximize the usage of existing IT infrastructure and analytical platforms. If your existing approach to analytics no longer gives competitive advantage we often suggest exploring one of the available ML-as-a-service cloud solutions.
ML Audit and Consulting
Our broad industry experience in delivering analytics to Fortune 500 companies gives us confidence to be able to evaluate and assess your current analytical ecosystem and processes. If your company is struggling with adoption of ML or business benefits from AI are not to par, we are here to help. By evaluating every step of ML lifecycle in your organization we will be able to pinpoint the challenges and prepare a resolution plan. We summarize our findings in a formal report where we also outline recommendations for the organization and outline next steps to be taken. Our findings often include cost cutting guidelines, strategies for improving time to market team. We also define processes limiting amount of rework needed in the ongoing support of Machine Learning products.
Design and Implementation of AI Solutions
Core of our engineering services is delivering end-to-end AI tools that solve business critical problems. We can help your company gain competitive advantage in the following areas:
- Making predictions more accurate with AI
- Automating repetitive tasks
- Allocating marketing budgets more effectively
- Ensuring business continuity with AI-powered tools
- Understand patterns in your business data
- Secure your corporate data and IP from exfiltration
- And many more that we will be happy to discuss and explore
Team Augmentation
If your goal is to develop Machine Learning competences in-house, we can introduce our Data Science consultants to your team. DS Stream Data Scientist will provide you with the capacity to get the analytical work done faster while training others in the process. Us working in proximity with your in-house staff will enable easy knowledge transfer and present a lot of learning opportunities.
With our extensive practical experience, we can provide comprehensive services for Apache Airflow
Design
Platform:
- On premises or in the Cloud
- Hardware scaling
- Fault tolerance
Software:
- Required components
- Choosing an approach to prepare the optimal workflow build
Security:
- Implementation of SSO authentication
- key vault utilization for credentials or sensitive data storage
- Designing multiple levels of access for specific target groups
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Installation
- Installation of all prerequisites on the selected platform
- Airflow installation in the selected environment: bare metal, virtual machines, containerized in Docker, orchestrated via Kubernetes
2
Airflow implementation
- Building DAGs using Python: static or dynamic
- Building custom operators not available out of the box
- Setting up automated monitoring or alerting for whole workflows or selected tasks
- Building custom user interfaces integrated with Airflow (using JavaScript) via dedicated operators that dynamically trigger building specific tasks or workflows based on user input
- Monitoring the DAG execution progress, access to logs, and other functions
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Testing and debugging
- Testing implemented solutions
- Error debugging, including source code analysis both for an implemented solution and Airflow itself
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Benefits of Data Science & Advanced Analytics services
Become a data-driven organization with the meaningful insights required to make business decisions immediately.
How can your organization benefit from Data Engineering services?
- Making better decisions based on the powerful knowledge you gain from the data
- Improving product quality and user experience by analyzing gathered data
- Identifying new business opportunities by predicting behaviors based on historical data
- Reducing costs and increasing profits by simplifying your data architecture
- Reducing of project duration by speeding up the processes of accessing insights
Our Clients
Here are some of our clients that trust us with their online solutions
Our Technology Tool Stack
Cloud Toolset
- Analytical Databases: Big Query, Redshift, Synapse
- ML SaaS offerings: GCP AI platform, Amazon SageMaker, Azure MLMLOPS ToolsKubeflow, MLflow
Visualisation Tools
- Python: numpy, pandas, matplotlib, scikit-learn, scipy, spark, pyspark & more
- Scala, Java
- SQL, T-SQL, H-SQL, PL/SQL
Visualisation Tools
- Power BI
- Tableau
- Data Studio
- D3.js
Data platforms
- Snowflake
- Azure Synapse
- Databricks
- Bigquery
- Salesforce
- Sap
- Teradata
Data Science & Advanced Analytics FAQ
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1. What is Data Science?
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2. What is the difference between Data Science and Big Data?
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3. What is the role of a Data Scientist?
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4. What is the future of Data Science?
Data Science is a multidisciplinary field that combines domain expertise, programming skills, and knowledge of mathematics, statistics, and machine learning algorithms to extract insights and make predictions from large and complex data sets.
Big Data refers to the volume, velocity, and variety of data that organizations collect and process. Data Science, on the other hand, is the process of analyzing and extracting insights from Big Data using various techniques and tools.
A Data Scientist is responsible for identifying business problems, framing them as analytical questions, and applying the right analytical techniques to derive insights and recommendations. They work closely with stakeholders to communicate findings and collaborate with developers to implement solutions.
The future of Data Science is bright, as organizations continue to generate more and more data, and the demand for insights and predictions increases. Some emerging trends in Data Science include the use of AI and machine learning algorithms for automation and decision-making, the integration of Data Science with other disciplines like Business Intelligence and Operations Research, and the adoption of cloud-native and serverless architectures for scalability and cost-effectiveness.
