Top 5 Tools for MLOps

April 14, 2022

MLOps is defined as "a practice for data scientists and operations experts to collaborate and communicate to help manage the production ML (or deep learning) lifecycle." MLOps boosts the automation and improves the quality of production ML while simultaneously concentrating on business and regulatory needs, similar to DevOps or DataOps."

Still confused?

MLOps offers the insights that you can rely on and can put them into action more rapidly, just as DevOps simplifies production life cycles by delivering better products with each iteration.

In short, MLOps stands for Machine Learning Operations combined with DevOps to develop solid automation, tracking, monitoring, pipelining, and packaging solution for Machine Learning models.

The topline benefit of machine learning is an organization's ability to stay relevant and develop in today's digital and information-driven environment, among many other benefits. This capacity gets enhanced expeditiously when integrated with operations to form MLOps.

There are numerous positive impacts of MLOps, and a few of them are:

  • Machine learning lifecycle management that allows for rapid innovation
  • Make workflows and models that are repeatable.
  • High-precision models may be deployed in any area with ease.
  • The complete machine learning lifecycle is well managed.
  • Control and administration of machine learning resources.

The below image depicts the process of MLOps as a whole:

Below is the list of the top 5 Tool for MLOps, that are assisting businesses and individuals in their growth.

  1. Kubeflow

Kubeflow aims to make machine learning (ML) workflow deployments on Kubernetes as simple, portable, and scalable as possible. Its goal is to make the deployment of best-of-breed open-source machine learning systems easy and simple on a variety of infrastructures. Kubeflow can be run anywhere Kubernetes is installed.

Benefits of Kubeflow:

  • Create and manage interactive Jupyter notebooks with Kubeflow's services. You may tailor your notebook deployment and compute resources to your specific data science requirements.
  • You may train your machine learning model with Kubeflow's custom TensorFlow training job operator. Kubeflow's job operator, in particular, can handle distributed TensorFlow training jobs.
  • To export trained TensorFlow models to Kubernetes, Kubeflow provides a TensorFlow Serving container.
  • Kubeflow Pipelines is a complete solution for delivering and managing machine learning processes from start to finish.
  • Kubeflow goes beyond TensorFlow in terms of support. PyTorch, Apache MXNet, MPI, XGBoost, Chainer, and other libraries are supported.

2. MlFlow

MLFlow is an open-source platform that allows you to manage the entire machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry. It is integrated with a variety of Machine Learning libraries, such as TensorFlow, Pytorch, and much more. This integration makes training, deployment, and maintenance of Machine Learning applications easier.

Benefits of MLflow:

  • Data science code is packaged in a way that allows it to be reproduced on any platform.
  • Machine learning models can be used in a variety of service scenarios.
  • In a central repository, you may save, annotate, discover, and manage models.

3. Data Version Control (DVC)

DVC is an open-source Data Science and Machine Learning application built in Python. It uses a Git-like paradigm to handle datasets and machine learning models, as well as versioning them. It makes machine learning models reproducible and shareable. It's built to work with huge files, data sets, machine learning models, metrics, and code.

Benefits of DVC:

  • Machine learning models, data sets, and intermediate files are all version-controlled. DVC employs code to connect them and stores file contents on Amazon S3, Microsoft Azure Blob Storage, Google Drive, Google Cloud Storage, or disc.
  • A project in DVC has a cleaner structure since it permits branching as simple and fast as Git — regardless of the size of the data files.
  • Lightweight pipelines are introduced by DVC. It allows you to utilize push/pull commands instead of ad-hoc scripts to transport consistent bundles of machine learning models, data, and code into production, distant computers, or a colleague's machine.
  • Every ML model's whole evolution may be tracked with full code and data provenance.

4. Metaflow

Netflix created a Python/R-based application named Metaflow, which was made open-source in 2019. It makes building and managing enterprise Data Science projects simple.

Metaflow simplifies the creation and management of real-world data science initiatives. To rapidly train, deploy, and maintain ML models, Metaflow unifies Python-based Machine Learning, Deep Learning, and Big Data libraries. 

Benefits of Metaflow:

  1. Metaflow assists you in designing your workflow, scaling it, and deploying it to production.
  2. It automatically versions and tracks all of your experiments and data.
  3. It makes it simple to inspect findings in notebooks.
  4. Metaflow has built-in connections with Amazon Web Services' storage, computation, and machine learning services.

5.  Pachyderm

Pachyderm is a version-control tool for Machine Learning and Data Science that works similarly to DVC. It's built on  Kubernetes and Docker, which makes it easy to run and deploy Machine Learning applications on any cloud platform. Every piece of data input into a Machine Learning model is versioned and retraceable with Pachyderm.

Benefits of Pachyderm:

  1. It specializes in structured data, which allows for an AI-driven business model.
  2. Models can be easily built on top of the data warehouse.
  3. NLP should be accelerated. Data-driven automation for development
  4. Handle even the largest unstructured and structured data sets with ease.
  5. Reduce model risk by ensuring complete reproducibility.

The Bottom Line

Effectively using machine learning is more than crunching numbers or trusting your data scientists to figure out compliance and business intelligence on their own. It's critical to take ownership of production-level machine learning so that your operations staff understands and knows this new era of data which will help the data team focus on what they do best. Looking forward to operations ensures that you're ahead of the machine learning curve and that your adoption is seamless and insightful right away.

MLOps is one of the most helpful practices a company can have because it automates everything from data sourcing, data processing, analysis, scalability, auditing, and prediction monitoring. It helps the organization in production model deployment, model monitoring, life cycle management of the model, and the governance of the model as well. 

Many open-source frameworks have arisen in the few short years that pushed MLOps to gain prominence. As technology and data continue to reach new heights, implementing solid ML strategies now will help enterprises of all types manage and prosper in the future.

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A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

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  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
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All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

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The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

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By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

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So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

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GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

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  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
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  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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