Key Aspects of Machine Learning Operations, Explained | Hacker Noon

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If you have ever worked or currently working in the IT field, then you definitely faced the common term «machine learning.

Machine learning is a general method for extending and optimizing the development options functionality, widely applied since the 50s of the 20th century. Until 2015, even professional programmers didn’t consider machine learning has real potential and benefits.

However, with the innovations in the database science field, the development of AI and computing capabilities build-up, autonomous MLOps platforms began to develop rapidly and became an integral part of computer systems development.

Three Main MLOps Aspects

Currently, MLOps defined as «a communication and cooperation involving both data scientists and development specialists. Used for manufacturing product lifecycle management. As with DevOps and DataOps, MLOps aims to enhance the functionality of ML products by concentrating on the regulatory and technical requirements of today’s development.

Modern MLOps characterized by three main aspects:

  1. DevOps (IT)
  2. Machine Learning
  3. Database architecture

Considering the modern progress of MLOps methods, the potential of MLOps is enormous. Currently, MLOps consists of whole full-fledged tests and taxonomies that give specialists an understanding of the system performance and keys to each individual iteration process.

How MLOps Works

Today, MLOps largely follows the standard DevOps scheme. Advanced machine learning methods and practices combine cyclical software developing and timely integration of new algorithms, which fundamentally changes the data processing.

In general, it is comparable to DevOps — reducing and overall optimization of the product life cycle by creating more complex versions of the software. Also, MLOps provides an understanding of the machine learning process and its basic algorithms, which contributes to wider coverage of methods for working with data.

It’s no secret that data is a key tool for business. The improvement of MLOps processes contributes to the creation of more accurate forecasts detailing information about each bit of information. Successful integration of MLOps solutions requires active collaboration between programmers and database engineers — while having a flexible enough mind to understand the information processing model when implementing machine learning.

MLOps Main Advantages

Among the main MLOps positive features, there are several main ones, directly related to the ability of any company to actively develop products even in the narrowest market conditions and at a galloping pace of technological progress. Most experts point out the following advantages of MLOps techniques:

  • Integrate innovation quickly and easily by robustly managing all aspects of the ML life cycle
  • Creation of a comprehensive model of the workflow
  • Easily deploy high-precision manufacturing models anywhere
  • Effectively managing every aspect and moment of the product’s ML life cycle
  • Management and control of ML resources

The Main MLOps Complications

As exciting as ML may seem at first glance, the integration of these techniques is often quite difficult. The main reasons are difficulties in code and database combining to achieve the proper level of predicting system behaviour.

According to expert data, the main difficulties of working with MLOps are:

  • Deployment and automation
  • Reproducible of models and predictive diagnostics
  • Scalability
  • Collaboration between the different professionals
  • Business models usage
  • Monitoring and management

According to the run.ai portal, most companies have never implemented the full MLOps cycle. The main difficulty, which became a stumbling block and commonly — the end of the implementation of the methodology — is the construction of correct and productive cooperation between database specialists and developers. Some require a clear data model for building the system architecture, while others require optimized methods for integrating machine learning into the process of developing the functionality of an application or other software product.

Nevertheless, taking into account the detailed analysis of MLOps practices «under the microscope», it is possible to synthesize the optimal model of cooperation between different specialists and achieve the set goals in the shortest possible time.

Successful MLOps Practices

Co-team Operations

One of the most common challenges with MLOps is establishing a productive collaboration model between diverse development departments. In this situation, core management needs to create a «hybrid» development team that is equally well versed in databases and development processing.

The popular online magazine Towards Data Science notes:

“The levels of professionalism and the main field of work of specialists can differ significantly, but when building a complex team that covers all the requirements of MLOps techniques for knowledge of system architectures and direct development processes, it is possible to implement this machine learning model by 100%.

In addition, every data specialist must handle basic developer skills — code modulation, reuse, testing.”

Such a versatile collaboration will provide productive synergy across multiple development departments.

MLOps Pipelines

The source framework for database design includes pipelines, which are precursors to retrieve, transform, and load information. Formatted as infographics depicting each individual node of development, these pipelines are the most important parts of database management. Using MLOps, data transformation is required by default, and such process pipelines are a key standard for working with information.

Monitoring

As for MLOps, focusing on actual processes is much more important than manufacturing processes. Basic math functions are used at the root of ML processing. The development black box needs attentive monitoring to ensure that work is proceeding within the norms and the programs are returning correct information about product development quality. In MLOps, monitoring is controlled by the following factors — latency, traffic, errors, and attention to these factors has the highest priority.

Versioning

In the traditional world of software development, only correctly versioned code is used since the behaviour of programs of different versions is determined by it. The versioning process is quite different in MLOps. In addition to default code, you should keep track of model versions, training data, hyper-parameters.

Validation

Testing is a critical part of the process of integrating DevOps practices. Data and finished models don’t require validation. Since the assembled product structure itself doesn’t show any results, the tests must be statistical and carried out in the appropriate segments to correctly reflect the data. While approaching data, tests should be conducted to the highest standards. Correct statistical testing in all fields of MLOps provides spectacular results.

The Future

Numerous open-source platforms have emerged in recent years. Currently, the popularity of MLOps opens up cloudless prospects for expanding the capabilities of analytics of the software development process. Through the comprehensive integration of leading-edge solutions, all kinds of organizations can effectively manage the development process and add more success.

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