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cuelebre-sweden · 2 years

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Cuelebre: Increase Your Business ROI With AI-Powered Data Analytics

We, Cuelebre is the Best AI & Data Engineering Services Company in Sweden. Improve your business strategy with help of AI & Data Analytics

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#data analytics #AI-Powered Data Analytics #Business ROI #Strategic Consultation #Data & Platform Engineering #Data Science AI/ML Models #Advanced Business Analytics #DataOps and Maintenance

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rawcubes · 1 year

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Five Reasons Why Predictive Machine Maintenance is a Critical Pillar of Success for Modern Manufacturers

In the contemporary manufacturing industry, a multitude of processes necessitate swift, top-notch performance and productivity, which poses a formidable challenge for factory workers. Hence, machinery is one of the most essential foundations of the manufacturing sector, and its consistent and efficient operation plays a pivotal role in achieving success. However, even the slightest malfunction, breakdown, or minor issue can result in significant ramifications for the entire production process, ultimately affecting the company's profitability.

Predictive maintenance utilizes IoT-based resources to monitor equipment health and ensure production stays on track. It is an approach that leverages machine data and advanced analytics to predict and prevent potential equipment failures before they occur. This proactive maintenance approach can increase machine uptime, reduce maintenance costs, and optimize the use of maintenance resources.

By shifting from reactive maintenance to proactive maintenance, companies can achieve significant cost savings and improved equipment reliability. Predictive maintenance is becoming increasingly popular in the manufacturing industry, and with the growth of IoT, AI, ML, and other advanced technologies, it is likely to become even more widespread.

The following reasons emphasize the pivotal role of predictive machine maintenance in achieving success in the manufacturing industry:

● Minimized Downtime

● Maximized Efficiency

● Ensured Quality

● Increased Employee Safety

● Reduced Cost

Examples of Major Disruptions in Manufacturing Due to Machine Failure:

Production delays: Any malfunctioning machine can lead to significant production delays, causing missed deadlines and resulting in lost revenue.

Downtime costs: Replacing faulty equipment can be time-consuming and may require additional costs such as overtime pay for workers or renting temporary machinery to continue production.

Quality issues: A machine breakdown can result in defective products that fail to meet quality standards, leading to customer complaints or reduced sales.

Safety concerns: Certain equipment failures can pose safety risks to employees or the environment, resulting in costly accidents or legal liabilities.

When unpredictable events such as machine failures halt manufacturing production, it leads to downtime, and the challenge for the industry is maintaining all types of equipment while keeping costs low. Predictive machine maintenance is a solution that addresses this issue.

Two Use Cases on Why Predictive Machine Maintenance is a Critical Pillar of Success for Modern Manufacturers

Predictive machine maintenance is a crucial factor for success in modern manufacturing, as highlighted by various studies. According to McKinsey, PdM software can reduce production equipment downtime by 30% to 50%, and extend machine life by 20% to 40%. The U.S. Department of Energy also estimates that preventive maintenance can result in significant benefits, such as up to a 30% reduction in energy and maintenance costs, 35% to 45% fewer breakdowns on machinery, and up to 75% reduction in downtime.

Rawcubes iDataOps provides manufacturers with two use cases that demonstrate how predictive maintenance can be beneficial.

Machine Data Integration for Early Detection of Potential Failure: iDataOps enables you to connect and integrate data from all machines with predictive maintenance techniques, allowing you to monitor equipment performance in real time, identify issues, and make predictive decisions. With this tool, you can connect production across the shop floor for real-time visibility of key machine metrics, transfer data using different protocols, and align machine data and service providers with predictive asset maintenance.

Preventive Maintenance Management for Reduced Downtime: iDataOps enables you to create proactive maintenance schedules as per each machine's specifications, helping to prevent future breakdowns or emergency maintenance issues. Predictive maintenance tools can minimize overall maintenance costs, optimize spare part inventory, and reduce downtime. This tool can notify production support and technician staff of scheduled physical check-ups, easily recognize local service technicians for more streamlined troubleshooting, and digitally maintain previous records aligned with the QR code to verify all details of services.

The Final Thought!

Accurate data is critical to the success of predictive maintenance in modern manufacturing. It improves the accuracy of forecasts, enhances decision-making, reduces errors, and boosts efficiency.

Rawcubes' iDataOps offers machine monitoring software that enables manufacturers to harness accurate data from their machines for predictive analysis. The platform provides turn-key predictive equipment analytics services, eliminating the need for large data integrations.

With iDataOps, technicians, leaders, workers, and CEOs can leverage the predictive machine maintenance platform to increase efficiency and reduce operational downtime.

Empower your maintenance team with Rawcubes iDataOps by reserving a complimentary demo today!

#Industrial DataOps machine monitoring Software #Machine Monitoring Platform #Preventive Maintenance Management #Predictive Machine Maintenance #predictive machine maintenance #machine monitoring #machine monitoring software #preventive maintenance #predictive maintenance #asset maintenance management

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atozofsoftwareengineering · 1 year

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Demystifying Data Engineering: The Backbone of Modern Analytics

Hey friends! Check out this in-depth blog on #DataEngineering that explores its role in building robust data pipelines, ensuring data quality, and optimizing performance. Discover emerging trends like #cloudcomputing, #realtimeprocessing, and #DataOps

In the era of big data, data engineering has emerged as a critical discipline that underpins the success of data-driven organizations. Data engineering encompasses the design, construction, and maintenance of the infrastructure and systems required to extract, transform, and load (ETL) data, making it accessible and usable for analytics and decision-making. This blog aims to provide an in-depth…

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sigmasolveinc · 2 months

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Data Engineering Trends for Maximizing Data Potential

In today’s data-driven world, businesses are constantly seeking ways to stay ahead of the competition, make informed decisions, and create value from their data assets. Data engineering plays a pivotal role in this journey, as it involves the collection, transformation, and delivery of data to make it accessible and actionable for various stakeholders. To excel in this dynamic landscape, organizations must adopt a proactive approach to data engineering, embracing emerging trends and technologies that enable them to not just keep up but lead the way. In this article, we will explore some of the key data engineering trends that empower organizations to take a proactive stance towards their data initiatives.

DataOps: Streamlined Data Operations

DataOps is a methodology that aligns data engineering, data integration, and data quality practices with DevOps principles. This trend emphasizes automation, collaboration, and continuous integration and delivery (CI/CD) processes for data pipelines. By implementing DataOps, organizations can reduce development cycle times, enhance data quality, and ensure that data pipelines are robust and scalable. This proactive approach enables teams to respond rapidly to changing data requirements and deliver high-quality data products to end-users.

Cloud-Native Data Engineering

Cloud computing has revolutionized data engineering by providing scalable, flexible, and cost-effective infrastructure for data storage and processing. Cloud-native data engineering leverages cloud services and platforms like AWS, Azure, and Google Cloud to build and operate data pipelines. This trend enables organizations to scale their data infrastructure as needed, reduce maintenance overhead, and focus on data engineering tasks rather than infrastructure management.

Serverless Computing

Serverless computing is gaining momentum in the data engineering space. It allows organizations to run code in response to events without managing servers. This trend simplifies data engineering by eliminating the need to provision, scale, or maintain servers, enabling teams to focus solely on writing code and developing data pipelines. Serverless architectures also offer cost advantages as organizations only pay for the computing resources used during execution.

Data Governance and Privacy

Data governance and privacy are critical concerns for organizations in the age of data regulations such as GDPR and CCPA. Proactive data engineering includes implementing robust data governance practices and ensuring data privacy compliance throughout the data lifecycle. This involves data cataloging, access control, encryption, and auditing to protect sensitive information while making data accessible to authorized users.

Real-time Data Processing

Real-time data processing is becoming increasingly essential for organizations to make instant decisions, detect anomalies, and respond to events as they happen. Data engineering trends like stream processing frameworks (e.g., Apache Kafka, Apache Flink) and real-time data analytics platforms (e.g., Apache Spark Streaming, AWS Kinesis) enable organizations to ingest, process, and analyze data in real time, providing valuable insights and actionable information promptly.

Data Mesh Architecture

The Data Mesh concept is gaining traction as a way to decentralize data ownership and improve data discoverability and access. It involves breaking down data silos and treating data as a product. Proponents of Data Mesh advocate for cross-functional, autonomous data teams responsible for data domains, making data engineering more proactive by distributing responsibilities and promoting data democratization.

Machine Learning Integration

Machine learning (ML) and artificial intelligence (AI) are reshaping the data landscape. Integrating data engineering with ML pipelines enables organizations to leverage predictive analytics and automation for data cleansing, transformation, and anomaly detection. A proactive approach to data engineering involves harnessing Machine Learning to optimize data processes and deliver data-driven insights more effectively.

Low-Code/No-Code Data Engineering

Low-code and no-code platforms are simplifying data engineering tasks by allowing non-technical users to design and execute data pipelines. These platforms empower business analysts and domain experts to be more involved in the data engineering process, accelerating the development of data solutions. This trend promotes a proactive approach by reducing bottlenecks and increasing collaboration between technical and non-technical teams.

Data Quality and Monitoring

Proactive data engineering requires robust data quality and monitoring practices. Organizations must implement data profiling, validation, and cleansing processes to ensure the accuracy and reliability of data. Additionally, proactive monitoring and alerting systems can detect data issues in real time, enabling swift resolution and minimizing data-related disruptions.

Automated Data Documentation

Documenting data pipelines and datasets is essential for maintaining transparency and ensuring data lineage. Automated data documentation tools are emerging to streamline this process, making it easier for data engineers to keep track of changes, dependencies, and lineage. This proactive approach enhances data governance and facilitates compliance with regulatory requirements.

In conclusion, a proactive approach to data engineering is essential for organizations looking to harness the full potential of their data assets. Embracing these data engineering trends enables businesses to stay ahead of the curve, respond to changing data needs, and drive innovation. By adopting DataOps methodologies, leveraging cloud-native solutions, and integrating emerging technologies, organizations can build a data engineering foundation that not only meets current demands but also positions them for future success in the ever-evolving data landscape.

#real-time data analytics #cloud services #Machine Learning

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abangtech · 4 years

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Machine Learning challenges in legacy organisations – Finextra

Fans of machine learning suggest it as a possible solution for everything. From customer service to finding tumours, any industry in which big data can be easily accessed, analysed and organised is ripe for bringing about new and compelling use cases. This is especially attractive for legacy organisations, such as financial services firms, looking to gain an advantage.

These businesses are usually well embedded in their markets, fighting with competitors over small margins and looking for new ways to innovate and drive efficiency. They also have an abundance of historical and contemporary data to exploit. One asset any start-up lacks is owned historical data, which gives legacy firms an edge in the competitive landscape. The promise of machine learning is therefore particularly seductive – feed in your extensive customer and business insights along with your desired outcome and let algorithms work out the best path forward.

However, established businesses such as these are also the ones that can face the biggest challenges in driving value through machine learning due to technical debt, poor infrastructure and low-quality data, leading to higher costs of deployment as well as higher maintenance costs.

Take a legacy financial institution as an example. Though the organisation may have extensive historical data, much of it may be held in old documents and unstructured formats. Without effective data mining capabilities, both in terms of expertise and technology, this data will remain largely unusable. It’s only when dedicated data science teams and tools are put to work that this value can be unlocked.

At a recent developer meetup, I heard from Teodor Popescu, a Data Engineer at BBC about how he deals with these issues for the nation’s biggest broadcaster.

Too much of a good thing

“There is so much hype about machine learning, but no one talks about the infrastructure behind it,” explained Teodor.

In every machine learning project, the raw material is high quality data. In legacy businesses, while there may be a lot of data around, it is often unstructured, incomplete or hard to find. IBM confirms that for most companies, 80 percent of a data scientist’s time is spent simply finding, cleansing, and organizing data, leaving only 20 percent to actually perform analysis and to run the clean data through a model.

Large volumes also lead to issues with scaling, as Teodor found when training a machine learning model on three billion data points. Infrastructure struggles to keep up with the volume of information, while processes that deploy and track the results need to be scaled at the same time.

The power of pipelines

At the BBC, there are over 1.3bn events generated every day. This requires machine learning teams to focus a lot of their time finding, maintaining and expanding sources of reliable data.

By working with third party integrations, teams can mitigate some of the existing issues around data management by sourcing new, structured data. However, these integrations still require maintenance, with broken pipelines slowing down development and deployment.

Instead of focusing solely on how to bring more data in, organisations can instead focus on the infrastructure for managing the data internally.

Tailoring systems

There are two approaches to this problem: specific data infrastructure and specialised team structure.

One example is personalisation. In order to maximise speed, click data from iPlayer is channeled through a distributed streaming service (such as Apache Kafka, Amazon Kinesis or a collection of Amazon SQS services) and processor Apache Spark in sequence, before being delivered to storage in AWS or back to iPlayer, via API, so that personalised options can be presented back to the user.

This is also reflected in the way of structuring data science teams, introducing DataOps and MLOps to take on specific roles.

These teams work behind the scenes to enable better performance across the data science teams, focusing on the robust implementation of data version control, ensuring adequate testing is conducted for both models and the data; working to accelerate the journey to machine learning deployment and reproducibility.

Given the specialised nature of many of the systems, developers can play a key role in determining what is possible, efficient and valuable for the organisation. Legacy organisations looking to leverage their data therefore need to focus on specific issues and datasets in order to deliver targeted solutions efficiently, rather than taking a broad approach. Machine learning models are only as good as the data that feeds them, so establishing the use case for specific datasets is vital.

Finding the right problems

Despite the difficulties, machine learning can still be an incredibly valuable tool for legacy businesses. The key to success is tailoring your approach to data and tools to the specific needs of the organisation.

The goal for internal development and data science teams in this process is to align the business on goals, methods and infrastructure. In essence, fully understanding a business problem is the key to creating the right use cases for any data project, This is the only way to ensure robust processes once in production, while managing scope and efficiency. In this way, teams can build incremental gains that drive long-term value throughout the organisation.

Source

The post Machine Learning challenges in legacy organisations – Finextra appeared first on abangtech.

from abangtech https://abangtech.com/machine-learning-challenges-in-legacy-organisations-finextra/

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nicaurybenitezcortorreal-blog · 7 years

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How to get started with machine learning: Use TensorFlow

Machine learning is still a pipe dream for most organizations, with Gartner estimating that fewer than 15 percent of enterprises successfully get machine learning into production. Even so, companies need to start experimenting now with machine learning so that they can build it into their DNA.

Easy? Not even close, says Ted Dunning, chief application architect at MapR, but “anybody who thinks that they can just buy magic bullets off the shelf has no business” buying machine learning technology in the first place.

Data in, intelligence out: Machine learning pipelines demystified•Google’s machine-learning cloud pipeline explained•R and Python drive SQL Server 2017 into machine learning. | Keep up with hot topics in programming with InfoWorld's App Dev Report newsletter. ]

“Unless you already know about machine learning and how to bring it to production, you probably don't understand the complexities that you are about to add to your companies life cycle. On the other hand, if you have done this before, well-done machine learning can definitely be a really surprisingly large differentiator,” Dunning says.

Open source projects like TensorFlow can dramatically improve an enterprise’s chances of machine learning success. TensorFlow “has made it possible for people without advanced mathematical training to build complex -- and sometimes useful -- models.” That’s a big deal, and points to TensorFlow, or other similar projects, as the best on-ramp to machine learning for most organizations.

Machine learning for nothing, predictions for free

Machine learning success rates are so low because “machine learning presents new obstacles that are not handled well by standard software engineering practices,” Dunning says. A successful dataops team involves complicated lines of communication and a multipronged development process.

Couple those complexities with the reality that machine learning systems “can easily have hidden and very subtle dependencies,” and you have a perfect form for things going awry.

Google, which knows the payoffs and pitfalls of machine learning more than most, has written about the hidden technical debt imposed by systems that use machine learning. As the Google authors stress, “It is common to incur massive ongoing maintenance costs in real-world machine learning systems.” The risks? “Boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, configuration issues, changes in the external world, and a variety of system-level antipatterns.”

And that’s just for starters.

Not surprisingly, software engineering teams are generally not well-equipped to handle these complexities and so can fail pretty seriously. “A good, solid, and comprehensive platform that lets you scale effortlessly is a critical component” to overcoming some of this complexity, Dunning says. “You need to focus maniacally on establishing value for your customers and you can't do that if you don't get a platform that has all the capabilities you need and that will allow you to focus on the data in your life and how that is going to lead to customer-perceived value.”

The four ways that TensorFlow makes machine learning possible

Open source, a common currency for developers, has taken on a more important role in big data. Even so, Dunning asserts that “open source projects have never really been on the leading edge of production machine learning until quite recently.” With Google’s introduction of TensorFlow, a tectonic shift began.

Get started with Azure Machine Learning. | The InfoWorld review roundup: AWS, Microsoft, Databricks, Google, HPE, and IBM machine learning in the cloud. ]

But TensorFlow’s (as well as Caffe’s, Mxnet’s, and CNTK’s) shaking of the foundations of the machine learning orthodoxy is not the big deal, in Dunning’s opinion. No, “the really big deal is that there is now a framework that is 1) powerful enough to do very significant projects, 2) widely accepted and widely used, and 3) provides enough abstraction from the [underlying] advanced mathematics.”

His first point – the power to do real machine learning projects -- is a gimme. Being limited to very simple models is not the way to stage an machine learning revolution.

His second point, however, is more surprising: “The point is that we need a system to be used by a wide variety of teams working on a wider variety of problems to have enough people write simple examples for newbies. We need a system that becomes a standard for accompanying implementations with machine learning papers so that we can tell where the paper glossed over some details.”

His third point about abstraction is also very important: “The fact that program transformation can produce code that implements a derivative of a function efficiently was not at all apparent even just a short while ago.” But it’s critical. “That capability, more than anything else -- including deep learning -- has made it possible for people without advanced mathematical training to build complex -- and sometimes useful -- models.”

With TensorFlow and other open source projects like it, teams can acquire new skills to successfully deploy machine learning by iterating and experimenting. This willingness to get hands dirty with open source code is his fourth point, that “successfully deploying machine learning will require that a team is willing to look deeply into how things work.”

Real machine learning success, in other words, isn’t going to come from an off-the-shelf software package, no matter how hard the company markets it as such (think IBM’s Watson).

Recommendations for doing real machine learning

For those that are ready to embark on a real machine learning journey, TensorFlow is a great way to get started. As you embark on that journey, Dunning has two recommendations:

First, prioritize logistical issues, a model delivery framework, metrics, and model evaluation. “If all you have is a model and no good data and model execution pipeline, you are bound to fail.”

Second, “immediately dump the myth of the model. You won't have a single model for one function when it all gets into production. You are going to have multiple models for multiple functions. You are going to have subtle interactions. You are going to have to be able to run a model for quite some time to make sure it is ready for prime time. You are going to need to have perfect histories of input records. You are going to need to know what all the potential models would respond to input.”

Source

http://www.infoworld.com/article/3212944/machine-learning/how-to-get-started-with-machine-learning.html

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cuelebre-sweden · 2 years

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We, Cuelebre is the Best AI & Data Engineering Services Company in Sweden. Improve your business strategy with help of AI & Data Analytics

Strategic Consultation

Data & Platform Engineering

Data Science AI/ML Models

Advanced Business Analytics

DataOps and Maintenance

Source: cuelebre.se

#data analytics #AI-Powered Data Analytics #Business ROI #Strategic Consultation #Data & Platform Engineering #Data Science AI/ML Models #Advanced Business Analytics #DataOps and Maintenance

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cuelebre-sweden · 2 years

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We, Cuelebre is the Best AI & Data Engineering Services Company in Sweden. Improve your business strategy with help of AI & Data Analytics

Strategic Consultation

Data & Platform Engineering

Data Science AI/ML Models

Advanced Business Analytics

DataOps and Maintenance

#data analytics #AI-Powered Data Analytics #Business ROI #Strategic Consultation #Data & Platform Engineering #Data Science AI/ML Models #Advanced Business Analytics #DataOps and Maintenance

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cuelebre-sweden · 2 years

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We, Cuelebre is the Best AI & Data Engineering Services Company in Sweden. Improve your business strategy with help of AI & Data Analytics

Strategic Consultation

Data & Platform Engineering

Data Science AI/ML Models

Advanced Business Analytics

DataOps and Maintenance

Source: cuelebre.se

#data analytics #AI-Powered Data Analytics #Business ROI #Strategic Consultation #Data & Platform Engineering #Data Science AI/ML Models #Advanced Business Analytics #DataOps and Maintenance

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cuelebre-sweden · 2 years

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We, Cuelebre is the Best AI & Data Engineering Services Company in Sweden. Improve your business strategy with help of AI & Data Analytics

Strategic Consultation

Data & Platform Engineering

Data Science AI/ML Models

Advanced Business Analytics

DataOps and Maintenance

Source: cuelebre.se

#data analytics #AI-Powered Data Analytics #Business ROI #Strategic Consultation #Data & Platform Engineering #Data Science AI/ML Models #Advanced Business Analytics #DataOps and Maintenance

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cuelebre-sweden · 2 years

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We, Cuelebre is the Best AI & Data Engineering Services Company in Sweden. Improve your business strategy with help of AI & Data Analytics

Strategic Consultation

Data & Platform Engineering

Data Science AI/ML Models

Advanced Business Analytics

DataOps and Maintenance

Source: cuelebre.se

#data analytics #AI-Powered Data Analytics #Business ROI #Strategic Consultation #Data & Platform Engineering #Data Science AI/ML Models #Advanced Business Analytics #DataOps and Maintenance

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cuelebre-sweden · 2 years

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DevOps | DevOps Engineer | Cuelebre

We, Cuelebre is the Best AI & Data Engineering Services Company in Sweden. Improve your business strategy with help of AI & Data Analytics

Strategic Consultation

Data & Platform Engineering

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#data analytics #AI-Powered Data Analytics #Business ROI #Strategic Consultation #Data & Platform Engineering #Data Science AI/ML Models #Advanced Business Analytics #DataOps and Maintenance

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cuelebre-sweden · 2 years

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Business Analytics | Business Analytics Software | Cuelebre

We, Cuelebre is the Best AI & Data Engineering Services Company in Sweden. Improve your business strategy with help of AI & Data Analytics

Strategic Consultation

Data & Platform Engineering

Data Science AI/ML Models

Advanced Business Analytics

DataOps and Maintenance

#data analytics #AI-Powered Data Analytics #Business ROI #Strategic Consultation #Data & Platform Engineering #Data Science AI/ML Models #Advanced Business Analytics #DataOps and Maintenance

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cuelebre-sweden · 2 years

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Machine Learning | Artificial Intelligence | Data Science | Automated AI | Cuelebre

We, Cuelebre is the Best AI & Data Engineering Services Company in Sweden. Improve your business strategy with help of AI & Data Analytics

Strategic Consultation

Data & Platform Engineering

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#data analytics #AI-Powered Data Analytics #Business ROI #Strategic Consultation #Data & Platform Engineering #Data Science AI/ML Models #Advanced Business Analytics #DataOps and Maintenance

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cuelebre-sweden · 2 years

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Data Engineering & Data Analytics Services | Cuelebre

We, Cuelebre is the Best AI & Data Engineering Services Company in Sweden. Improve your business strategy with help of AI & Data Analytics

Strategic Consultation

Data & Platform Engineering

Data Science AI/ML Models

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DataOps and Maintenance

#data analytics #AI-Powered Data Analytics #Business ROI #Strategic Consultation #Data & Platform Engineering #Data Science AI/ML Models #Advanced Business Analytics #DataOps and Maintenance

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cuelebre-sweden · 2 years

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Data, AI & ML Business Consultant: AI Powered Data Analytics Company in Sweden

We, Cuelebre is the Best AI & Data Engineering Services Company in Sweden. Improve your business strategy with help of AI & Data Analytics

Strategic Consultation

Data & Platform Engineering

Data Science AI/ML Models

Advanced Business Analytics

DataOps and Maintenance

Source: cuelebre.se

#data analytics #AI-Powered Data Analytics #Business ROI #Strategic Consultation #Data & Platform Engineering #Data Science AI/ML Models #Advanced Business Analytics #DataOps and Maintenance

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