Evolving ML Pipelines

A Pipeline is rudimentarily an independent executable workflow of a machine learning task. The deployment of Machine Learning (ML) algorithms is an arduous and time-victualing process that comprises a chain of sequential and correlated responsibilities that bypass from the data pre-processing, and the layout and extraction of features, to the cull of the ML algorithm and its parameterization.

Tech enthusiasts are keen to set up the features of DevOps for AI and ML projects. Implementing MLOps shows the automation and monitoring of each and every single step of the ML system building. It is predicted by the analysts that building an ML model is not the real challenge, the real challenge is devising an integrated ML system and to continuously run it in production.

Why are ML pipelines important??

There are the predefined steps but before that, you need a whiteboard to pin down your sane approach towards your organization ML pipeline requirements. the term ‘pipeline’ is deceiving because it implies a unidirectional drift of data. Rather, machine learning pipelines are cyclic and redundant as every step is repeated to constantly enhance the accuracy of the model and attain a booming algorithm.

According to researchers and analysts, every company has different needs when it comes to the ML systems. The data for a project is collected from various sources hence it is of varied format as well. We know that in a Data Science a pipeline runs from consuming and cleaning of the data, to feature engineering and model selection in an interactive environment, to training and distribution of results, then deploying those trained models to produce results in the form of predictions and classifications.

In Computer Science the breaking a task into “modules” is of great significance. Hence, Pipelines are assigned to break ML tasks into smaller modules and supporting this notion that a component should “ only one thing at a time”. An ML project has many responsibilities such as data preparation, access, cleaning, training, model deployment, and monitoring, etc., The output of one task acts as an input of another. In its computational state, a bug in one of them can affect the other one as well. Managing these complex pipelines is getting harder each day, specifically when you’re using real-time data and have update models more frequently. There are dozens of various tools, libraries, and frameworks for machine learning to know, and every data scientist has their own unique set that they more adapted to, and they all are combined differently with data stores and the platform’s system learning models run in.

Applying features of DevOps in MLOps

Remember when developing and deploying a software project used to be a painful task, fast forward to 20 years later today ML applications resemble them greatly.

Back then the response loops were distinctly long and by the time you launched an application, the necessities and designs that you began with were outdated.

Then, in the late ’00s, DevOps surfaced as a set of software engineering best patterns to manage the software development life-cycle and alter continuous, rapid transformation.

DevOps is the union of both software development and operations with the targets of reducing the generic delivery time of solutions while sustaining a good user experience through automation (e.g. CI/CD and monitoring). MLOps is a new term that expresses how to apply DevOps rules to automate the building, testing, and deployment of ML systems. MLOps intents for the unification of ML application development and the operation of ML applications, making it easier for groups to deploy finer models more often.

The primary issue concerning the Data Scientists and Analysts today is that on differentiating the current pipelines we are aware that how different these pipelines are meant to be in the future of data science. The current ones are no proving to be sustainable, and you have to future proof yourself in order to become progressively equipped of dealing with these sorts of issues that we will be dealing within a couple of years. The pipeline for the deployment of new models may take many days or even months with numerous models never reaching production. Data Scientists are more involved inside the solution of relevant technologies. There are many examples when ML pipeline is applied to a real-time business problem where streaming data and prediction are of utmost importance (e.g. Netflix’s recommendation engines, Uber’s arrival time estimation, Airbnb’s search engines, etc).

Challenges faced by MLOps

In contrast to software engineering, data science is still lacking in getting their projects into production more frequently, because they do not have a well-designed and automated process for it.There are many already several end-to-end ML frameworks that support orchestration frameworks to run ML pipelines: TensorFlow Extended (TFX) supports Airflow, Beam, and Kubeflow pipelines.

Data scientists require the appropriate raw data for modeling and they end up spending a lot of time just searching for it, even while working on it data scientists and engineering teams have a lot of disagreements. Once the data is collected data scientists work for weeks into training and labeling. After this it is again given to the engineering teams for “production” of feature data pipelines i.e, making it production-ready. Then it goes for integration and deployment, back to the engineering team for monitoring to ensure that the ML model and data pipelines continue to operate properly.

Due to this, there is a lot of discontentment in Data Scientists because:

• They lack full ownership of products. Have to depend on others for deployment and production.

• Unable to iterate rapidly teams on which data scientists depend have their own priorities and plans, which regularly introduce deferrals and vulnerability. Iteration velocity is essential and delays can intensify to stages that crush profitability.

• Unable to identify performance issues  It is easy to miss on details when an engineer works on data scientists work especially if the model is not making accurate predictions because either the data pipelines have been broken down or model needs to be retrained.

Conclusion

It is wise to conclude that MLOps still has a long way to go. Because what once was to produce an ML model now is the first step in the long process of bringing it till the production. Today Data Science has become a part of every possible business application because the real-time data is being constantly generated 24/7, the response timings need to be up-to-date, and constantly increasing traffic on sites, etc., Applying Features of DevOps in machine learning has decreased the workload by tonnes as models are introduced in the market faster and quality is also present at a specific standard. To subdue the prototype phase, smooth, automated, and dependable operations have to exist. Thus, we may see much higher adoption in 2020 for MLOps.

Here at Datatron, we offer a platform to govern and manage all of your Machine Learning, Artificial Intelligence, and Data Science Models in Production. Additionally, we help you automate, optimize, and accelerate your ML models to ensure they are running smoothly and efficiently in production — To learn more about our services be sure to Book a Demo.