- With core specialisation in data engineering and AI solutions since 2013, Sigmoid has developed expertise to handle end-to-end machine learning projects, including automated data integration, building data pipelines, deploying ML models into production and monitoring model performance.
New-age technologies like artificial intelligence (AI) and machine learning have profoundly impacted various business applications. However, organisations are still struggling to scale AI projects because deploying machine learning models into production can take a long time. According to a recent report, only 53 per cent of projects make it from AI prototypes to production.
There are various reasons behind the failure, such as lack of adequate skills, insufficient data, and lack of organisational understanding of the utility of AI, among others. However, one primary reason why these projects fail is the execution of a weak AI strategy.
Making the deal out of data
Data is an important asset to an organisation. Extracting useful insights from the raw data can help businesses make better decisions. Here the role of data science becomes instrumental in analysing large amounts of unstructured and structured data.
Sigmoid is one such leading data solutions company that takes the data game to the next level and helps transform data into business goals. The company offers a custom AI strategy built with a combined effort of business consultancy, data science and data engineering capabilities for the specific business requirements. Sigmoid has also been recently figured in the Financial Times list of The Americas’ Fastest Growing Companies 2021.
Let us take a deep dive into how this data solutions company has successfully executed AI solutions and delivered tangible results for Fortune 500 companies.
One step ahead in the game
Setting itself apart from its competition, Sigmoid delivers tangible business outcomes for issues where other analytics vendors and internal data science teams have struggled, such as putting ML models into production. Since 2013, Sigmoid has developed expertise to handle end-to-end machine learning projects, including data management, model training, testing, deployment, and performance monitoring with core specialisation in data and analytics.
“At Sigmoid, we are passionate about delivering tangible results to some of the most pressing challenges faced by our clients. Our success in projects is driven by our commitment to building world-class talent in data science. We provide in-house training to help them keep in sync with the latest trends and best practices, translating into the overall success of the project,” says Rahul Kumar Singh, Co-founder and Chief Analytics Officer at Sigmoid.
With a unique blend of data science expertise, experience in deep cross-industry AI implementation and the ability to build production-level predictive machine learning services, Sigmoid empowers its customers with real-time analytics.
“So primarily the process we follow based on our knowledge and experience has been the biggest reason why we achieve results and businesses develop trust based on actual results that gives us differentiation against the competition. We bring results for the problems they have already tried solving and failed/hit a brick wall in improvement,” adds Rahul.
Data Science team at Sigmoid
The Data Science team at Sigmoid is built on five pillars — talent, cutting-edge technology training, problem-solving capabilities, innovation, and impeccable execution.
- Talent: A large population of the data science team at Sigmoid belongs to tier I institutes like IITs. Sigmoid looks for candidates with programming, statistics, and mathematics backgrounds, focusing on logical reasoning, data interpretation, and a problem-solving mindset.
Soft skills are crucial too. “Since our team is required to work with customers and cross-functional teams, there is a strong focus on collaboration skills and good communication, especially the ability to ask questions to refine problem statements,” says Rahul.
- Cutting-edge technology training: The talents at Sigmoid are trained in diverse techniques such as traditional supervised and unsupervised learning, including Random Forest, SVM, logistic, pattern mining, clustering, and reinforcement learning, including Multi-Armed Bandit and Monte Carlo.
The teams also gain experience in traditional techniques like BFS, DFS, heuristics, pruning, and new-age AI techniques like CNN, RNN and GAN that help them develop a 360 degree perspective of the AI field.
- Problem Solving: Sigmoid’s problem-solving approaches are based on 3Cs — critical thinking, curiosity, and creativity.
These include a strong focus on problem understanding, abstraction development, divide and conquer, causality/root-cause identification, constraint identification, hypothesis validation, test and learn methods.
- Innovation in every solution: Sigmoid builds bespoke solutions for the customers through a deep understanding of the business problems in the context of the overall functioning of the microenvironment connected directly or indirectly with the project.
“We look to hire talent that constantly pushes us to innovate every day,” adds Rahul.
- Execution: By combining the three core groups of data science, data engineering and DataOps teams, Sigmoid works on end-to-end analytics business problems.
Takshashila – Sigmoid’s Learning Academy
Sigmoid has been heavily investing in providing regular training to all its employees.
Takshashila is Sigmoid’s in-house learning academy that aims to provide employees with opportunities to acquire new skills — reskilling and to enhance their current skills — upskilling. Takshashila fosters application-oriented learning with best-in-class resources and platforms. Entry-level employees can start their journey at Sigmoid with an eight-week data and analytics training program, which covers business essentials, technical skills, soft skills, and mock projects to apply their learnings to real problems. We follow a blended learning approach that includes instructor-led classroom sessions and peer to peer knowledge sharing.
Experienced lateral recruits undergo customised training for 6-8 weeks, depending on their skill level. Sigmoid provides ample opportunities for data scientists to grow through using real-time data to solve end-user problems. Domain knowledge training is given by subject matter experts based on project requirements.
All new employees at Sigmoid are assigned a buddy and a mentor to navigate through the organisation and understand the project and processes.
Sigmoid enables business transformation using data and analytics, leveraging real-time decisions through insights by building modern data architectures using cloud and open source technologies. Combined expertise in Data Engineering with Data Science gives Sigmoid the competitive edge over large competitors and in-house teams in delivering analytics services to various sectors, including retail, CPG, BFSI, advertising, and healthcare.
With the majority of businesses across industries struggling to get their AI projects off the ground, addressing data challenges and data engineering pitfalls are critical to deploying AI/ ML models in business use cases — and that’s where Sigmoid comes into play.