DataHour: Prediction to Production in Machine Learning

September 13, 2022

The main objective behind building an ML model is to resolve an issue. It is only possible if the ML model is in production and regularly used by consumers. Bridging the gap between data science and IT can help in this regard. Both aspects could be connected if an ML model is deployed easily. This is possible through tools like Kube-Flow, TFX and Mlflow that can streamline the entire method of model deployment.

How to put machine learning models into production?

Data scientists need to assess the methods of putting ML models into production so that they can clearly understand the practices and methods and then go ahead.

  • From model to production

Before beginning any project, the ML team should consider three things:

  1. Storing the data and retrieving it
  2. The tooling and the frameworks involved
  3. The iteration after the feedback
  • Storage of data and retrieving it

An ML model is useless without data. You will have data sets for training, evaluation, testing and prediction. Now answer the following question

  • How is the training data kept?
  • What is the size of the data?
  • How to retrieve the data for the training?

The probable answers to these could be:

  • Data storage can be done on-premise or in cloud storage. The storage could also be done on a hybrid system.
  • If you have a large dataset, then additional computing power is required for pre-processing steps and for optimising the model. This means you have to plan the entire process from the beginning.  
  • Will you opt for batch data retrieval, or will you retrieve data in real-time? You have to do this before defining the ML system. Your prediction data is relative to the training data, and the packaging is also neat.
  • Frameworks and tooling

You need frameworks such as Scikit-Learn, Pytorch and Tensorflow for training models. The programming languages used will either be Go, Java or Python. You can even use cloud environments like Azure, GCP or AWS. But which tools would you choose for transferring and continuity of the model?

That can be decided with the help of three factors:

  • The efficiency of the framework or production tool. How do they use the memory and CPU for a specific duration?
  • The popularity of the tool in the developer community. This means the framework and tool perform well and have much client support.
  • Support for the tool or framework. Is it open source or close sourced? How fast can you find tutorials for mastering it for usage in actual projects?
  • Iteration and Feedback

ML projects are a part of design and engineering that are extremely critical from the very beginning of the project. You need to know how to get feedback from an in-production mode and how you can set up continuous delivery. This way, you can track the model state and be notified when the model does not produce optimally.

Continuous testing and deployment of new models minus interruptions in the existing model processes are referred to as continuous integration.

Final Words

Putting an ML model into production is not a complex process. You have to just have strategies and understand what works best for you. If you wish to learn more about these or want adequate infrastructure for your ML project, visit E2E Networks’ website.

Reference Links

https://towardsdatascience.com/machine-learning-for-production-optimization-e460a0b82237

https://stackoverflow.blog/2020/10/12/how-to-put-machine-learning-models-into-production/

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Lower TCO: Cloud GPUs require no upfront investment, making them ideal for companies that are looking to reduce their overall capital expenses. Furthermore, the cost of maintenance and upgrades is also low since it takes place in the cloud rather than on-premises.

Scalability & Flexibility: With cloud-based GPU resources, businesses can scale up or down as needed without any penalty. This ensures that they have the resources they need when demand spikes but also saves them money when there is little or no demand for those resources at all times.

Enhanced Capacity Planning Capabilities: Cloud GPU platforms allow businesses to better plan for future demands by providing estimates of how much processing power will be required in the next 12 months and beyond based on past data points such as workloads run and successes achieved with similar models/algorithms etc... 

Security & Compliance : Since cloud GPUs reside in a remote datacenter separate from your business' core systems, you are ensured peace of mind when it comes to security and compliance matters (eigenvector scanning / firewalls / SELinux etc...) 

Reduced Total Cost Of Ownership (TCO) over time due to pay-as-you-go pricing model which allows you only spend what you actually use vs traditional software licensing models where significant upfront investments are made.

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This is a decorative image for: Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian
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Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian

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Increase in Cloud Costs Due to Strong Dollar

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Possible Indian Alternatives to Cloud Services

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Conclusion

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Actions CEOs can take to get the value in Cloud Computing

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And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

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The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

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  1. Taking risks into consideration 

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It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.

Conclusion

In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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