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What Is MLOps?
Data Science

What Is MLOps?

4 minute read | July 4, 2020
Sakshi Gupta

Written by:
Sakshi Gupta

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Machine learning is quickly evolving and playing crucial roles in companies of all sizes. To get the full benefit of machine learning models, you must first put them in the production pipeline; this comes with many challenges, especially when done on a large scale.

These challenges cannot be fully managed by existing DevOps practices because difficulties with machine learning systems are fundamentally different. That’s where Machine Learning Operations (MLOps) comes into play.

A quick guide to MLOps. We’ll discuss the challenges and benefits of MLOps, and available frameworks and platforms.

What is MLOps?

In simple terms, MLOps is a set of best practices to successfully run AI-driven products for industries. MLOps combines well-established DevOps practices with the continuously growing field of machine learning, finding new solutions to create an automated platform (or environment) for data gathering, model training, model testing, model deployment, model monitoring, model retraining, feedback system, and so on.

Challenges of the industry before MLOps

Here are some challenges encountered in current machine learning systems, before MLOps:

  • Dependency on the data changes with models and business objectives. This results in a lack of regularity and performance deterioration.
  • Periodic training is required for machine learning models as real-world data changes with time. Therefore, this periodic model training requires setting up complex pipelines that feed data from data lakes.
  • Difficulty managing configurations for machine learning projects like determining the data and features used, model parameters, verification methods, and so on.
  • Risk of machine learning models not performing well in real-time, which will require monitoring metrics continuously.
  • Collaboration issues can occur among business analysts, data engineers, data scientists, operations teams, and so on due to their competing needs. For example, business analysts focus on KPIs, whereas data scientists focus on the accuracy of models.

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6 benefits of MLOps

Here are six potential benefits of using MLOps systems:

  1. MLOps makes the orchestration of different pipelines possible. This is useful for developing and updating the machine learning model continuously. For example, data pre-processing, feature engineering, model training, inference, and so on.
  2. MLOps help manage the lifecycle of machine learning projects. The lifecycle of a machine learning project includes different parts that can be considered as different entities, which are challenging to manage by traditional DevOps practices. MLOps brings technology, people, and processes together to resolve this problem.
  3. MLOps regulates deployment at scale. Currently, the problem lies not in development but in the deployment of machine learning models, especially when required to deploy and manage thousands of models into production.
  4. MLOps maintains the health of ML models at scale. MLOps establishes a system that autonomously maintains the health of machine learning models at scale. Sustaining the health of deployed machine learning models is a crucial job. Deployed models need continuous monitoring for behavior on real-world data and should be able to detect problems like model drift, data drift, and so on, automatically.
  5. MLOps provides CICD for machine learning projects. Continuous integration and continuous deployment (CICD) were made possible by DevOps practices for software products; however, for machine learning products, the traditional DevOps practices cannot be used for CICD, due to scale and its manner of operation. This generates the need for MLOps systems to provide CICD for machine learning projects.
  6. MLOps systems provide model governance solutions. This is crucial, especially in big organizations where machine learning models are deployed on a global scale and manually tracking rules, regulations, and compatible versions of the machine learning model become almost impossible.

Available MLOps framework and platforms

Here are some well-known MLOps framework and platforms:

  • Algorithmia. Algorithmia helps to deploy, manage, and scale machine learning projects by providing machine learning workflows and procedures for IT, data scientists, and other teams that coordinate machine learning models in production. It retains model versioning, maintains pipelines, and supports deploy models at scale. However, for now, it doesn’t offer assistance for model training.
  • SageMaker. SageMaker is a fully managed service offered as part of AWS. It lends support for model training, debugging, hyper-parameter tuning, deployment, and monitoring as part of its studio environment.
  • Azure Machine Learning. Azure Machine Learning is provided as part of Microsoft Azure. It includes assistance for training, hyper-parameter tuning, deployment, and monitoring. In addition, it also supports drag and drops functionality to define machine learning workflows.
  • Google Cloud AI Platform. Google Cloud AI platform is a part of Google Cloud. It provides end-to-end support from model training to deployment and management. It also features an AI hub that includes many public resources, such as trained models, datasets, guides, notebooks, etc.
  • MLflow. MLflow is an open-source platform supported by Databricks and used to manage the lifecycle of machine learning projects. It has different modules, namely tracking, projects, models, and registry.

To clarify, Algorithmia and MLflow are MLOps frameworks, whereas SageMaker, Azure Machine Learning, and Google Cloud AI are complete platforms for MLOps.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.