Building Foundation for machine learning across your organization

There seems to be an obsession across various industries and technology sectors to implement and use Machine Learning for performing day to day tasks and activities. All things ML has swept the technology, business communities and society more broadly. An organization who enables ML has been witnessing several benefits that ML provides. The ability to harness and make use of large swaths of data for optimizing previously tedious tasks and making them more efficient and much more easy.

 

Having a core foundation that is solid is extremely important for organizations that are keen on implementing ML. The implementation of ML can be challenging even with mature engineering strength. There can be pitfalls and misconceptions in making any attempts in order to make the jump between ML research and ML in production environments. A frequently overshadowed as well as the under-appreciated aspect of getting it right is mainly the infrastructure that is known to enable robust and well-managed research and is able to serve customers in production applications.

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A major lever in setting up and configuring the foundation for having a successful ML implementation program is being able to build a culture and an atmosphere that allows you to try these efforts at scale. ML is able to accelerate the rate of scientific experimentation and set the road to production and ultimately bring value to businesses. The cloud is also an integral part of this effort and it is known to enable teams to develop as well as deploy well-governed and accurate ML models to high volume production environments. Apart from production deployments,  having a solid infrastructure provides a way for large scale testing of models and frameworks that is able to allow for greater exploration of the interaction between deep learning tools and data patterns and also enables teams to hire onboard new developers and ensure that changes to future models do not have any masked effects.

 

In this article, we will outline some of the tactical and procedural guidelines that help to set the foundation to bring effective ML into production across your enterprise using automated mode integration and deployment.

 

High-Level Challenges and Production Concerns using ML

Learning and implementing ML can be complex enough in production environments and only increases more when considering the necessity of addressing adversarial learnings. This is a subfield of ML that explores applications under hostile conditions. For example cybersecurity and money laundering. There can be adversarial attacks that range from causative to exploratory, and these encourage your model to change in response to carefully devised inputs and reduces efficacy.

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In the areas of cybersecurity and other complex domains, decision boundaries will frequently require robust context for human interpretation, and modern enterprises of any size are able to generate far more data that humans can analyze. Even the absence of such kind of adversarial concerns, user activity, network deployments and the simple advances of technology can cause data to pile up over time.

Keeping this in mind, production ML concerns are found to be universal. Data and model governance is able to affect all models, and retraining is nothing but a fact of life. So automating the production process is the main key for sustainable performance. 

 

Common Production concerns that need to be solved when setting up an ML foundation includes the following concerns:

 

  • Model problems in production: Models need to be trained, also updated and need to be deployed seamlessly. However, issues can arise with the use of disparate data sources, and using multiple model types in production (supervised/unsupervised) and using multiple languages in implementation.
  • Temporal Drift: Changes in data over time.
  • Context Loss: Model developers can forget their reasoning over the passage of time.
  • Technical Debt: This is known to cause issues in productive learning environments. ML models are complex to be fully understood by their creators, and this is even more difficult for employees who may not be ML experts. Automating this process  is known to minimize depth

The ideal system is known to address the technical overarching ML production considerations while addressing common adversarial concerns that includes

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  • Historical model and data training
  • Model monitoring as well as  accuracy tracking over time
  • Working with distributed training systems
  • Custom tests for each model to validate the accuracy
  • Deployment to production model servers

 

Model Management and Setup for a technical foundation

While there may be different requirements for each organization, these are high-level consideration for enabling effective model management:

 

  • Historical data training that provides fine-grained controls
  • Training functionality that is distributed
  • Support for multiple programming languages
  • Robust testing and reporting support
  • Easy to understand model accuracy
  • The model feature set, code tracking, and methodology
  • Provenance for data and definitions for internal data
  • Tooling that is open source
  • Custom retrain and loss functions using a cron-like basis in order to refresh stale models
  • Negligible impact on model developers as well as dedicated ML engineers

 

The Benefits and Practise of a Solid ML Foundation

 

Once all of the technical components are in place, it is crucial to ensure that proper practices and protocols are followed in order to continue reaping the benefits of a well-designed ML foundation

 

One major area is model governance. This is known to cover everything from ethical concerns to regulatory requirements. Your aim should be to make the governance process go as smoothly as possible. Similarly, historical tracking is also a key concept here and helps to delegate temporal drift. Model tracking over the passage of time is difficult and requires fine grained temporal data and a distributed model logging framework.

 

With the use of historical data tracking,  retrain and loss thresholds are provided by the user and these are used to automatically refresh models over time. In turn, this leads to more seamless model reproducibility- the immediate ability to generate historical models for validation against the current data conditions and a strong understanding of where the drift has occurred and the areas that it has affected. Furthermore practicing journaled knowledge retention mitigates context loss and ensures that the models are retrained and published automatically based on time changes to the underlying code as well as simple updates are easily identified.

 

Conclusion

To successful implement, Machine Learning requires a strong foundation for every organization and depends on its ML experts to have this foundation in place. This can lead to the efficient use of ML to automate mundane tasks and provide a conclusion out of data models that are analyzed for ML.

 

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nimap October 24, 2019 0 Comments

How Enterprise Software Is Getting Intelligent Through Machine Learning

Developments in computing have caused Machine Learning(ML) to come closer to what writers and inventors foresaw a long ago. AI, ML and Neural Networks have become the focal points of innovations and developments in computing. They have moved closer in order to achieve their full potential and are currently finding applications in almost every field of human activity from transportation to agriculture. Furthermore, these are the evolving forms and they have a notable effect in the enterprise and computing field too.

 

Among them, ML has found widespread acceptance among many enterprises and organizations. By integrating ML into enterprise software, many companies have discovered a much more predictable and data-driven model, that allows them to automate their business processes. Machine learning’s core dynamic of utilizing data sets has helped generate insights, that promote innovation, efficiency and have made enterprise software much more intelligent.

 

With ML, computers can mine out and process small to huge quantities of data in order to come up with predictive models and insights. A typical example that you can find of ML at work is in e-commerce where online retailers like Amazon are using this technology for offering better services to its customers.

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Each time you make a purchase or browse through the products list, you allow to  leave a trail of data used by its ML systems in order to give you personalized product recommendations. Besides, the use of ML is giving e-commerce platforms a clear edge when it comes to customer service, pricing, as well as delivery.

 

Why Machine Learning will impact Enterprise Software

Every day, the organization or enterprise is riddled with challenges from management to operations. Enterprise software up to now has played a major role in tackling many of these vital processes. But the rising demands in business have meant a new approach is needed to deal with the diverse fields. With the introduction of ML into the enterprise, a window of possibilities was left open.

ML is providing the right framework that organizations can rely upon. The algorithms and applications used in a ML embedded computing system can easily bring out predictive insights by harnessing the huge data sets owned by these enterprises. These data include both structured as well as unstructured forms such as databases and the Internet.

According to a study by MIT Technology Review as well as Google, 60 percent of companies have already adopted an ML strategy into their computing infrastructure. 18 percent plan to implement ML within the next year.

 

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The switch to an ML software model has clearly benefited large corporations by giving improved and faster data analysis and insights. Besides, the complexity of ML is preventing companies from switching to such a model. Being currently in development a complete shift to an ML model can prove to be unwise. ML has several restrictions and isolations as its algorithms frequently encounter errors and resolving them is an ardent task due to its complexity.

Besides, it can take time for an ML embedded software to familiarise with the data for better predictions. Meanwhile, these predictions may not always be accurate in all scenarios where historical data is not unavailable. The need for human intervention is still relevant to extract the desired output from ML enterprise software.

ML is identified as a major trend among enterprise software in the current year. By making enterprise software intelligent, ML can take over the crucial process of assessment by creating valuable insights. An enterprise or company can use this to take the necessary action to their productivity is increased.

Incorporating an ML framework into an enterprise software ensures all the data stacks are churned out for improving overall productivity and efficiency. This actually enhances the capabilities of software used in the enterprise thus making it much more intelligent as well as self-sufficient.

For example, in the area of sales, a company can gain a lot by using software having an ML model. ML can disrupt crucial processes and create more understanding of forecasting, customer behavior, and its assessment to make the sales process more intelligent.

Here are some ways by which ML will transform enterprise software are:

 

  • Generating better analytics and Insights

 

Data usage and data generation have increased considerably in organizations in recent years. Processing these large amounts of data is a huge challenge, which is where ML fits in. Having enterprise software with ML helps derive a competitive advantage from these large sets of unstructured data.

By supplying the data gathered, the algorithms in an ML embedded software can easily process these data sets and discover various insights as well as patterns. An organization can benefit greatly from these results by helping them in order to meet their objectives and drive the growth forward.

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The multinational retail corporation Walmart is a fine example of successfully implementing ML software. They utilize ML heavily to discover customer behavior and usage in areas such as product recommendation.

As a result. Walmart has dramatically improved its retail operations and customer experience. Besides, ML helped them create a bridge between their shopping experiences offered at their stores and online platform

 

  • Delocalization of Data

 

Data is now largely scattered due to the use of various technologies like mobile and social media. Typically, enterprise software used specific kinds of database models in order to store as well as organize data. Structured and unstructured data can now exist together in external sources, which requires deeper processing for enabling the systems to generate useful end results.

Technology giants like Google have already shifted entirely to external storage modules in the form of data centers for handling its massive troves of these unstructured and structured data. These data centers are located in different places around the world and require constant monitoring as well as maintenance to make them running all throughout without any disruptions.

Google has already utilized ML software to aid in this process, which has resulted in a significant decrease in energy usage amounting to a 50 percent reduction in 2014 alone. The tech giant was able to optimize and save much more energy in its data centers by using an ML model.

 

  • Facilitate Data-driven Decisions

 

The use of data in an effective manner can prove to be of immense advantage to a company in formulating crucial business decisions. Enterprise software systems that have been using ML can aid in this process greatly by helping companies and enterprises to make decisions solely and impartially based on the insights gathered from this pool of data. This will be of particular help to areas like talent hiring, customer management, R&D and so on..

For example, the major use of TensorFlow technology, Google’s open-source library for ML applications, has already enabled researchers departments in companies for getting yield in the potential of ML for delivering better insights to enable the development of products and services. The use of DMAIC (Define, Measure, Analyze, Improve and Control) is common when ML is applied in R&D to make data-driven decisions. This has led to a considerable improvement in the quality of products and services.

 

  • Empowers Employee Intelligence

 

 

ML takes enterprise software a step forward by automatically assessing the searches that employees in an organization make. This has proven to actually empower employee intelligence by facilitating decision making in areas related to work by monitoring all the crucial areas that they engage with.

In this manner, ML can assist in enhancing productivity, save time as well as give timely responses. For example, IBM has set a model on how cognitive technologies like ML can empower the employees by creating better engagement and personalized dissemination of information for purposes like coaching.

 

 

  • Fraud Detection

 

 

The continued presence of fraudulent practices contribute to losses to an organization every year. Enterprise software with an ML framework have helped bring down the fraud by detecting it early. The pattern recognition methods used by the ML algorithms can identify any irregularities in operations like transactions.

 

An example of how ML would help in fraud detection is in the financial section of an enterprise. By including ML in enterprise software, it can evaluate the transaction data and external sources to detect any kind of fraudulent activities as well as anomalies in the transactions involving a network of individuals.

 

SAS is an early adopter of ML in areas like fraud detection. In the finance sector, SAS has ventured into fraud detection and the use of  their software by several credit institutions to eliminate fraudulent practices in transactions.

 

Conclusion:

There is no doubt Machine Learning is taking over the world and helping organizations and corporates get the best out of their businesses. In the coming years, we will see ML be booming in every domain and sector possible, and helping our lives to be augmented and amplified further to improve the quality of life that we live.

If you are thinking to this all this as in real for your development then Hire Python Developers from Nimap Infotceh directly contact and get quote

 

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nimap October 23, 2019 0 Comments

Big Data Basics

Big Data essentially suggests to enormous amount of data that, when joined, surpasses points of confinement of conventional advances and procedures to make the information valuable. Right now organizations face critical difficulties in terms of V3 genuine mix and coordinated effort. That is a colossal missed opportunity. There are three “Vs”.

Volume  suggests to the quantity of information sources and the sheer volume of information put is expanding exponentially, Velocity suggests to the coming in great speedier rate than a large portion of us can envision and Variety suggests the distinctive sorts of information – both organized and unstructured.

Whatever Vs say, we have to know where the information is and have the capacity to get to and comprehend it anyplace, whenever. Without that capacity, you’re continually looking in the back perspective reflect and discussing what was rather than what can be. Can we have knowledge into existing information emulated by premonition?

Will Big Data empower us to quit taking a gander at business from what has happened till now (Descriptive) and why it happened (Diagnostic) and begin looking with a more proactive methodology (Predictive) that asks, “What will happen and in what manner would we be able to get it going?” (Prescriptive). Case in point Can we have a 360 degree perspective of your clients concerning their influencers and practices which prompts achievement business?

 

Execution of investigation’s system includes information, methodology, and individuals:

  • Information created from business forms. It is hard to unite information crosswise over diverse information storehouses. Is Will putting away more than 90 days of information not get to be lavish?
  • We have to accompany the procedure for discovering esteem in the information and the methodology for including dissection into the business. it is imperative that the process begins little and quick and fabricate validity. The procedure needs to utilize great visualizations to tackles an information venture.
  • The group needs learning of the issue space and one information researcher. Each colleague ought to have the capacity to force their information and do some basic dissection. They likewise ought to be raised on rate on the space learning asap. This group ought to have a chief or C level official as a colleague to impact and make required changes ready to go methodologies of the undertaking.

 

Be arranged to handle these difficulties:

  • Machine learning or Data Science can’t supplant individuals.
  • When you collect content, the volume, speed and assortment of data getting gathered is testing individuals’ capability to oversee it.
  • At the point when engagement with clients get to be more transparent, the back-end business process needs to be streamlined and improve

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nimap October 8, 2014 0 Comments
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