Big Data & Machine Learning Services

Our services enable you to identify the right data sets, case feature sets, and prediction labels, evaluate model performance, and optimise ML solutions for your development & production environments, allowing you to meet real-world challenges with near-zero risk, lower costs, and a high degree of certainty.

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From Pure Data to Business Expansion

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

Building and Deploying ML Models

The process of building models still – in most companies – follows the dreaded 80/20 rule. That is, 80 per cent 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) and throughout the model creation pipeline and augmented analytics.

The real hurdle for implementing ML models may not be all the steps leading up to it or the model evaluation and tuning itself, but rather 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 operationalisation of predictive and prescriptive models."

Gartner

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.

ML-driven software services are what turn your data from a cost centre into a critical enterprise asset.

Getting there is about going from the ability to operationalise one, two, or tens of machine learning models to hundreds or thousands of models that all work together to build the core business. Practically, this requires the right team, processes, and tools that enable proper monitoring, management, and tuning 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 enabling 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 operationalisation features to put models in production quickly, the ShakooMakoo team enables ML-driven software services in top companies worldwide.

Transforming The Customer Experience in Retail With ML & AI

The ShakooMakoo team enables retailers to scale MI implementation significantly by providing data availability to execute use cases such as dynamic item pricing, demand forecasting, and more.

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)

Challenges
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

• Personalised recommendation engines: Personalisation has become one of the top preferred features for any retailer's website, who are faced with fierce competition from e-commerce giants and an increasingly demanding customer base. AI & ML-powered retailers and brands utilise advanced ML algorithms to analyse browser history, page clicks, social interactions, past purchases, page viewing duration, location, and other factors to gauge customer interests and preferences in a more complex and exhaustive manner than previously possible.
• Price optimisation: AI and ML enable retailers to increase sales and boost their bottom lines through price optimisation. 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 optimisation: Moreover, AI & ML offer a powerful set of applications for retailers to enhance and personalise 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.
• Behavioural and geospatial analysis: Geospatial analysis is helping retailers better understand the interplay between their brick-and-mortar and online operations. Deep learning programs are capable of harnessing video surveillance of customers to analyse 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-personalised, automated marketing..
• Inventory management and stock optimisation: AI & ML will continue to optimise 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 visualisations 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 effectively it is at collecting, tracking, and measuring customer interactions. The ShakooMakoo team is highly effective at enabling users to collect, clean, and analyse these types of datasets for a 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 organisation 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 provides the opportunity to utilise AI and ML to gain valuable insights into the lifecycle of equipment in use. Rather than correcting problems once they occur, predictive maintenance prevents problems from ever happening in the first place.

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 clear that a 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 highly costly in and of itself (even when compared to the benefits of identifying necessary maintenance before run to failure), the following 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

Shakoo & Makoo Services aims to help enterprises build a platform that democratises access to data and enables them to develop their path to AI & ML. More than 150 companies across retail, e-commerce, healthcare, finance, transportation, the public sector, manufacturing, pharmaceuticals, and other industries use our services to scale their AI & ML efforts massively.

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 tuning to prevent model drift.