Big Data & Machine Learning Services

Our services enable you to identify the right data sets, case feature sets, prediction labels, evaluate model performance and optimize ML solutions for your development & production environments in order to meet real-world challenges with near-zero risks, less cost and a high degree of certainty.

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Benefits of Machine Learning Development

Get an edge over your competition by utilizing the power of more accurate predictions and business insights you get from data.

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Secure your niche by building and releasing cutting-edge applications and products faster.

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Gain insights into customer behaviour to customize your products, improve user experience, and increase customer satisfaction.

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Reduce operational costs by improving the performance of your team; improve your product’s performance using ML.

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Shakoo and Makoo Services

Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning development cycle. Your team needs to define the right case data context, stitch together tools and workflows, which is time-consuming and error-prone.

Shakoo & Makoo services help you to overcome these challenges by providing the right development approach to define & build the proper data components required for building models for each learning machine case.

Shakoo & Makoo services are smart big data & ML software services, respectively. These services formulate the successful set of steps & tools for the data layer and ML layer to deliver the underlying models and move to production faster and get more accurate prediction results with much less effort and at a lower cost.

Latest ShakooMakoo Services Projects for:

From Pure Data to Business Expansion

ShakooMakoo offers ML services which democratize the access to data and enabling enterprises to build their path to ML & AI in a human-centric way.

Data Exploration & Preparation

Empowering everyone in an organization to get insights from data is the first step toward Enterprise AI.

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Building and Deploying ML Models

The key to delivering business value from raw data lies not only in empowering every employee to leverage data-powered insights but also in the company’s ability to go beyond the limits of small data and create machine learning models at scale.

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Model Dynamic Tuning and Management

Creating real value from data means building - and maintaining - a spectrum of ML-driven applications and services that run as a core part of the business.

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Data Exploration & Preparation

ShakooMakoo services allow companies to take that step in their journey by powering self-service analytics, putting increasingly sophisticated data analysis in the hands of the many.

How ShakooMakoo Unlocks Insights

ShakooMakoo services are the path to democratize access to data and enable enterprises to build their path to ML implementation and deployment. Our team believes that those companies who succeed in deploying and scaling ML will do so by ingraining a culture of working with data throughout the enterprise instead of siloing it into a specific team or role.

To make this a reality, ShakooMakoo provides:

  • A straightforward approach for data purification, mining, visualization, machine learning, and deployment.
  • A collaborative and team-based task sharing between our team (data scientist and data engineer) and your team to define the required data sets.
  • Instant insights on datasets via our project reports, detailed dataset audit reports, and the ability to filter and search the right data.
  • Our team will use visual processors for code-free data manipulation and transformation that fits your development environment.

Our team believes that the more people are involved in ML development processes, the better the outcome. Our team brings collaboration at every stage, from ETL to model management.

Building and Deploying ML Models

The process of building models still – in most companies – follows the dreaded 80/20 rule. That is, 80 percent of the modelling process is connecting to data, cleaning, rearranging, and enriching it to get it into a state where a machine learning model can be applied. That leaves a lot of opportunity for automation in both the creation of models themselves (e.g., tuning hyperparameters) as well as throughout the model creation pipeline and augmented analytics.

The real hurdle for ML models implementation may not even be all the steps leading up to it or the model evaluation and tuning itself, but putting that model in production, a critical, yet challenging, piece of the conundrum.

"Companies still struggle to make the journey from data science interest and excitement to the operationalization of predictive and prescriptive models."


Practical Insights From Active Data Science Teams and Mature Machine Learning Strategists, Peter Krensky, 9 October 2019

Model Dynamic Tuning and Management

Creating real value from data means building - and maintaining - a spectrum of ML-driven applications and services that run as a core part of the business.

What Are ML-Driven Services?
Once an enterprise has the ability to quickly operationalize ML data projects and moves from a handful to hundreds (or thousands) of machine learning models in production, the question of maintenance and management arise. ML-driven services encompass the organization’s operationalized models plus systems – largely driven by ML running operation – ensuring models are well maintained, continually tuned, performing as expected, and not having any adverse effects on the business.

"87% of US CEOs are investing in AI initiatives this year."

EY: The AI Race.

ML-driven software services are what turns your data from cost center into critical enterprise asset.

Getting there is about going from the ability to operationalize one, two, or tens of machine learning models to hundreds or thousands of models that all work together to build the core business. Practically, that requires the right team, processes, and tools that will allow for proper monitoring, management, and tunning of models in production.

ShakooMakoo Team Provides the right ML-Driven Services for Your Business

ShakooMakoo Team is about building the right ML platform to access the appropriate data and enables enterprises to develop their path to machine learning. When it comes to streamlining and automating workflows, our services allow data teams to put the right processes in place to ensure models are correctly monitored and easily managed in production, including:

  • Model rollback management.
  • Deployment health monitoring.
  • Model drift feedback loop.
  • Model version management.
  • …and more.

Combined with robust operationalization features to put models in production quickly, ShakooMakoo team is the enabler of ML-driven software services in top companies worldwide.

Transforming The Customer Experience in Retail With ML & AI

ShakooMakoo allows retailers to scale MI implementation significantly by enabling data availability to execute on use cases like dynamic item pricing, demand forecasting, and more.

If you judged by the number of times the phrases “artificial intelligence” and “Machine Learning” were used at NRF 2019 – Retail’s Big Show and Expo, you would think that advanced solutions are being rolled out across every retail enterprise. And while implementing ML solutions in physical retail is naturally more challenging than online retail, taking a step back, both are still surprisingly only in early stages. However, this is about to change.

"The retail industry could reap global benefits from AI worth $400-$800 billion—more than any other industry."

McKinsey Global Institute.

With Enterprise AI, machine learning (ML) and data science solutions becoming a benchmark for business practices, retailers today have the unprecedented opportunity to shift the paradigm and leverage their data to elevate the customer experience in new, more meaningful ways.

AI & ML for Retail and Consumer Packaged Goods (CPG)

Nevertheless, the tremendous benefits of AI to the retail economy are inevitably accompanied by serious challenges, such as:

  • The high cost of putting models into practice.
  • Lack of resources and expertise in small and mid-size businesses.
  • Data collection.
  • ROI estimation.
  • Data governance.
  • Data-culture change.
  • …and more.

This may sound overwhelming, especially for midsize and smaller retailers, but it shouldn’t stop companies from embarking on their Enterprise AI & ML journey. By setting up the right infrastructure for their teams, tools, and processes, as well as working on high-value use cases, retailers can deliver real business value from their AI & ML initiatives using ShakooMakoo services.

High-Value Use Cases for AI in Retail

  • Personalized recommendation engines: Personalization has become one of the top preferred features for any retailer web site, who are faced with fierce competition from e-commerce giants and an increasingly demanding customer base. AI & ML-powered retails, and brands use advanced ML algorithms to analyze browser history, page clicks, social interactions, past purchases, page viewing duration, location, etc. to gauge customer interests and preferences in a more complex and exhaustive way than previously possible.
  • Price optimization: AI and ML enable retailers to increase sales and boost their bottom lines through price optimization. This method involves, on the one hand, tailoring prices to customers in a way that they view them as attractive, fair and non-arbitrary for the products they care most about, and on the other, predicting when it is or isn’t necessary to offer discounts.
  • Loyalty programs optimization: Moreover, AI & ML offer a powerful set of applications for retailers to enhance and personalize their loyalty programs. With capabilities like natural language processing, image analysis and semantic reasoning, marketers can benefit from having an adaptive and evolving understanding of the customer-to-brand engagement.
  • Behavioral and geospatial analysis: Geospatial analysis is helping retailers to better understand the interplay between their brick-and-mortar and online operations. Deep learning programs are capable of harnessing video surveillance of customers to analyze their behaviour and preferences. Such analysis could be linked to the purchase information of the customer –– either their credit card or loyalty card –– and be used to target them with hyper-personalized, automated marketing..
  • Inventory management and stock optimization: AI & ML will continue to optimize inventory for large and small retailers. Large companies are already pioneering technology that more precisely aligns inventory with customer demand. Inventory management is particularly salient in the grocery sector, where the ability to forecast demand reduces food waste and ensures that there is an adequate supply of the items that customers want at a given time..

ShakooMakoo Sevices for the Retail Industry

ShakooMakoo team helps your teams to establish the right access to data and enabling retailers to build their path to AI & ML. By making AI & ML accessible to a broader scope within the enterprise, facilitating and accelerating the design of machine learning models, and by providing a centralized, controlled, and governable environment, ShakooMakoo team allows retailers to massively scale AI & ML efforts, particularly through:

  • Dynamic Pricing. Predictive pricing models built that can include datasets from any data source, from complex SQL databases to simple Excel spreadsheets. This enables highly-targeted retail and online marketing forecast models that can show which price points are most likely to turn potential customers to proven buyers.
  • Demand Forecasting for Inventory Management. ShakooMakoo team can help retailers find the right balance by using demand forecasting to predict production and consumption quantities, by leveraging its user-friendly visualizations and web apps, to convey how your customers are likely to engage with your products based on the dimension of your choosing, and with datasets of your choosing.
  • Customer Service and CRM Initiatives. At the end of the day, it’s all about how successfully a company engages with its customers, which is measured by how effective it is at collecting, tracking, and measuring customer interactions. ShakooMakoo team is highly effective at enabling users to collect, clean, and analyze these types of datasets for better understanding of what specific changes need to be made.
  • Build a Better Recommendation Engine. Recommendation engines can be used across industries to provide value either to end customers or to employees of the organization itself.
  • Making the Transition into the age of AI & ML isn’t easy for the retail industry, but it also isn’t insurmountable. Retailers and brands that take a step-by-step approach and set themselves up with the right infrastructure for people, processes, and tools can thrive.

Transforming Predictive Maintenance with AI & ML

Predictive maintenance offers the opportunity to use AI and ML to glean powerful insights about the lifecycle of equipment being used. Rather than correcting problems once they occur, predictive maintenance prevents problems from ever happening in the first place.

With run-to-failure methods, low investment in maintenance leads to prohibitively high repair costs and downtimes once equipment does fail. With preventative strategies, constant, potentially unnecessary maintenance has very high costs without corresponding payoffs. With predictive maintenance, both high repair costs and time spent unnecessarily doing maintenance are minimized.

While algorithms are not perfect, they offer the opportunity to make maintenance choices that are based on past trends and real-time data; it offers an entirely new cost-saving dimension.

"In manufacturing, the greatest value from AI can be created by using it for predictive maintenance (about $0.5 trillion to $0.7 trillion across the world’s businesses)"

McKinsey Global Institute.

Second-Order AI & ML for Predictive Maintenance

Predictive maintenance generally requires another level of AI to optimize subsequent decisions about a high-value asset’s upkeep. Once it’s a clear repair of a high-value asset is necessary via predictive maintenance techniques using data from all kinds of sources, including Internet of Things (IoT) sensors, initial – perhaps automated – first steps or processes will be kicked off (things like filing a work order or notifying maintenance staff).

From there, that’s where second-order maintenance comes in. Because taking high-capital assets out of service can be extremely costly in and of itself (even when compared to the benefits of identifying necessary maintenance before run to failure), the next questions are when and how?

Take, for example, a truck from a large fleet with a part identified by your predictive maintenance system as being N days away from failure. Once identified, a member of the data team should be ready to send a secondary follow-up report to the maintenance team detailing the best possible options for time and place of service.

The ShakooMakoo Services Advantage

ShakooMakoo Services is to help enterprises to build a platform democratizing access to data and enabling enterprises to develop their path to AI & ML. More than 150 companies across retail, e-commerce, health care, finance, transportation, the public sector, manufacturing, pharmaceuticals, and more use our services to massively scale AI & ML efforts.

Customers building predictive maintenance solutions using ShakooMakoo services benefit from:

  • The ability to centrally and seamlessly connect to heterogeneous data, wherever it’s stored.
  • Establishing a simple and fast interface for ETL, including interactive data cleaning and integrated advanced processors.
  • The ability to evaluate and compare dozens of algorithms directly for supervised and unsupervised machines.
  • Reliable model deployment on the cloud with Kubernetes.
  • Robust model monitoring and dynamic tunning to prevent model drift.